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SPEECH PERCEPTION TEST FOR JORDANIAN
ARABIC SPEAKING CHILDREN
NADIA MOHAMED ABBAS ABDULHAQ
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
NADIA MOHAMED ABBAS ABDULHAQ
This dissertation is dedicated to my parents Abbas and Ursula.
I would like to thank my parents for their constant support and believe in me. They
have been a bottomless source of support, inspiration and encouragement. My sisters Ute
Nabila, Najat, Ulla Najwa, and brothers Mejdi and Uwe Maysarah, who where ever in the
world they were made sure to keep in touch and encourage me through the tough times
and share the joy of my success.
My friend from Palestine, Lubna Shaheen, for all the times she has listened to my
complaints and doubts and never let me go without giving me my confidence back. My
good friend, Abdelqader Abu Awad, for his support and encouragement from the time I
applied to the Fulbright program to the day of my graduation he has always been there for
There is a group of people here in Gainesville without whom I would have felt very
lonely. I would like to thank all, Debra Anderson, International student coordinator, for
being a shoulder to lean on, a good listener and a friend in times of need. She has been
my inspiration in many activities on campus that have brought diversity to my experience
and made it so much richer in people, memories, and achievements. My friends in
Gainesville, Abdullhatif Qamihieh for his readiness to help at any time what ever the task
was. Maisa Haj-Tas for being a wonderful understanding roommate and her stimulating
discussions and insight into research. Marah Al-Aloul, Ashraf Al-Qdah, Suha Abu Lawi,
Shadi Krecht, Yaser Katanani, Enas Katanani and Hadil Faqih, for their support and for
being my family away from home through out these past years, and my friends from the
Fulbright program and international students for being a renewing source of hope,
inspiration and fun.
This project was made possible by the continuous support by the faculty members
of the Departments of Communication Sciences and Disorders and Communicative
Disorders at the University of Florida. I would like to thank in particular my first mentor
Dr. Patricia Kricos for her guidance, support and generosity. Dr. Griffiths for his advise
through the graduate program in general and mentoring my dissertation in particular, Dr.
James Hall III, for his guidance, support and encouragement, Dr. Richard Harris for his
hospitality, generosity and guidance through the research process, and Dr. Tom Oakland,
for his honest opinions, believes in me and support. I would also like to thank Dr. Linda
Lombardino, Dr. Alice Dyson, Dr. Keneth Gerhardt, Dr. Mary Kay Dykes, and Dr. Aida
Bamia for their support.
Funding for this project was made possible by the Gibson Dissertation Fellowship,
offered by the Collage of Liberal Arts and Sciences at the University of Florida. The data
collection and field research was made possible by the generous help and collaboration of
Samar Al-Aghbar, the Jordanian ministry of education, and elementary schools of
Amman, as well as the support of the Middle East Hearing Association and their team
members, and Phonak hearing aid distribution office in Amman.
Completion of this degree was made possible by financial support from the
department of Communication Sciences and Disorders in the form of teaching
assistantship and Grinter Fellowship award for four years, as well as the College of
Liberal Arts and Sciences O. Ruth McQuown Scholarship for women, in addition to the
Fulbright scholarship that was the seed funding for this degree, and finally and most
importantly, my parents financial support.
TABLE OF CONTENTS
A C K N O W L E D G M E N T S ................................................................................................. iv
L IST O F TA B L E S ................. .......................................... .... .... ...... ...... ....... ix
LIST OF FIGURES ............................... ... ...... ... ................. .x
ABSTRACT .............. .................. .......... .............. xi
1 IN TR OD U CTION ............................................... .. ......................... ..
B background of the Study .................................................... .............................. 1
Rationale and Purpose ........................ ......... ... ..... ...... ....................
Hearing Loss in Developing Countries ...................................... ............... 3
H hearing Loss in Jordan ............................................................ ............4
Present Condition of Arabic Speech Audiometry ..............................................5
R e search Q u e stio n s.......................................................................................... .. 7
Experim ent O ne .................................................. ........ .. ........ .. 7
Experim ent Tw o ........................ .. .............. .......................... ....
H y p o th e sis ........................................................... ................ 7
Experim ent O ne .................................................. ........ .. ........ .. 7
Experim ent Tw o ........................ .. .............. .......................... ....
2 REVIEW OF THE LITERATURE .................................... .......................... .......... 8
Importance of Speech Audiometry ......... ............ ......... ................. 8
Speech A udiom etric Tools ............... ................. ................................................
Pediatric Speech Perception M materials .............................. ..... ............. 13
Adult Speech Perception M materials ........................................ ............... 14
Speech R exception Threshold M material ............................ .......... .................... 15
Full List and Half List U se ................................. .................................. 16
Arabic Speech Audiometry................. ................................. 18
Comparison of Arabic Speech Reception Tests ..................... ............... 18
Use of Arabic Speech Reception Tests .................... ........ ............... 20
D ialectal D differences in A rabic ............................................... ........ ............... 21
Special Considerations for the Present Study .................................23
P articip ants ........................................................................2 3
S p eak ers .................. ...................................................................................... 2 4
Stim uli for D ialectal D ifferences..................................... ......... ..............25
3 M E T H O D O L O G Y ............................................................................ ................... 29
P a rtic ip a n ts ........................................................................................................... 2 9
S p e a k e rs .................................................................................................... 3 0
Recording and Editing .................. ..................................... ...............3 1
Instrumentation for Data Collection ............................... .....................33
S tu d y O n e ............................................................................3 4
Speech M material ................. .... ...................... ......... 34
Procedure for Data Collection ................. .......... ....................35
Statistical A analysis .......................... .......... ................ ....... 36
Sp eech M material .............................. .......................... .... ........ .... ..... ...... 37
Statistical A analysis .......................... .......... ................ ....... 37
4 R E S U L T S .............................................................................4 1
S tu d y O n e .............................................................................4 1
S tu d y T w o ............................................................................................................. 4 3
5 DISCUSSION .......... .. .............. ...................57
A PARTICIPANT RECRUITMENT ....... ..................................... ........... 64
B SPEECH MATERIAL SELECTION ........................................................... 65
C LOGISTIC REGRESSION ANALYSIS ......................................67
D LIST OF WORDS FOR DIALECT COMPARISON .................... ..................68
E HALF WORD LISTS AND MEANING ...........................................69
LIST OF REFEREN CES ................................................................................... 71
B IO G R A PH IC A L SK E T C H ....................................................................................... 77
LIST OF TABLES
2-1 Children's speech reception tests in English ........................................................27
2-2 Speech recognition word lists' mean dB HL levels at 50% and slopes .................27
2-3 Speech reception threshold wordlists' mean threshold dB HL levels and slopes
betw een 20 and 80% ......... .................................................. ....... ........28
3-1 Order of list presentation by participant and level of presentation ........................39
3-2 W ord list and dialect presentation order. ..................................... ............... 40
4-1 Normal Hearing participants' age, gender, test ear and PTA.................................50
4-2 Half word lists and transcription in IPA....................................... ...................... 51
4-3 Logistic regression results for the full lists, the calculated slopes at the 50% and
the 20-80% levels, the threshold dB level, and difference of threshold levels
from the m ean threshold................................................. .............................. 53
4-4 Logistic regression results for the half lists, the calculated slopes at the 50% and
the 20-80% levels, the threshold dB level, and difference of threshold levels
from the m ean threshold................................................. .............................. 53
4-5 Selected characteristics of normal hearing participants .......................................54
4-6 Selected characteristics of hard of hearing participants................................54
4-7 Word recognition scores in percent correct for each subject per talker-talker and
the average of scores by dialect.................................................................... 55
4-8 Mixed ANOVA results comparing dialects for all participants' scores ...................55
4-9 Paired Sample T-tests comparing dialectal difference in the hard of hearing
group and norm al hearing group. ........................................ ........................ 55
4-10 Paired Sample T-test results including all participants' scores.............................56
5-1 Descriptive statistics for Hard of Hearing and Normal Hearing based on dialect ...63
LIST OF FIGURES
4-1 Average pure tone thresholds at all frequencies for all 20 normal hearing
participants at 250, 500, 1000, 2000, 4000, and 8000 Hz ......................................46
4-2 H alf lists raw data scatter plot ............................................................................... 46
4-3 Psychometric function of 4 lists of 50 words based on calculated percent correct..46
4-4 Psychometric function of 8 half lists of 25 words based on calculated percent
c o rre ct......... ........... .. ........... .. .................................................... 4 7
4-5 Arabic monosyllabic psychometric functions for lists 1-4 (left) and half-lists 1A-
4B (right). .............................................................................48
4-6 Average of pure tone thresholds of normal hearing participants at 250, 500,
1000, 2000, 4000, and 8000 H z. ........................................ .......................... 49
4-7 Average of pure tone thresholds of hard of hearing participants at 250, 500,
1000, 2000, 4000, and 8000 H z. ........................................ .......................... 49
Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
SPEECH PERCEPTION TEST FOR JORDANIAN
ARABIC SPEAKING CHILDREN
Nadia Mohamed Abbas Abdulhaq
Chair: Scott Griffiths
Major Department: Communication Sciences and Disorders
Comprehensive audiologic evaluation includes a variety of tests that provide a
determination of the type of hearing loss. Among these tests are tests of word recognition.
Many speech perception tests have been developed over the past half century to assess
different aspects of speech. Management of hearing loss in developing countries and the
USA differs significantly. The prevalence and demographics of the hard of hearing
population is also different, and thus imposes different needs. According to the World
Health Organization the number of deaf and hard of hearing individuals in developing
countries is twice as much as in developed countries. There is a need for early
identification and intervention for hearing loss in developing countries. At the same time
there is a severe lack of equipment and highly trained professionals to provide such
services. The focus of this study is speech audiometric measure for Jordanian Arabic
speaking children. The first goal of this study is to develop four Jordanian Arabic 50-
word lists appropriate to use for word recognition measure for Jordanian children age 6 to
9 years. The second goal is to investigate the effect of using words recorded in Saudi
dialect on the word recognition abilities of Jordanian normal hearing and hard of hearing
Twenty individuals age 6 through 9 years participated in this study. A Jordanian
speaker recorded 250 Arabic words selected for familiarity to elementary aged Jordanian
children. The raw score data of all participants at the 10 intensity levels were compiled
for each of the 250 words. Four equally difficult lists of fifty words each were derived
from this experiment. In the second experiment, 3 Jordanian and 3 Saudi male speakers
were selected to record 33 words. These were played back at a constant comfortable level
for 10 children with normal hearing and 10 children with hearing impairment who were
asked to identify the recorded words. Dialect produced a significant difference in
performance for children with normal hearing, but not for children with hearing
impairment. These results will lead to the development of improved techniques for
assessing auditory performance in Arabic-speaking children.
Background of the Study
An audiological evaluation typically includes measures of tympanometry,
acoustic reflexes, otoacoustic emission, and pure-tone (air and bone conduction)
threshold and speech audiometry. Results of these tests are used to diagnose hearing loss
and determine the type of hearing loss. Hearing testing has come a long way from
estimating hearing from the distance a person can hear a voice. Since the early 1900s,
pure-tone audiometry has been instrumental in measuring hearing sensitivity. The work
of Harvey Fletcher on the perception of speech in relation to sound level and noise effect
laid the foundation for decades of speech perception research (Fletcher, 1995). Speech
perception measures in the present format in the United States of America (USA) have
been used since the late 1940s and early 1950s (Egan, 1948; Haskins, 1949; Hirsh, Davis,
Silverman, Reynolds, Eldert, & Benson, 1952). Many speech perception tests have been
developed and improved over the past half century to assess different aspects of speech;
e.g., speech reception thresholds, speech pattern identification, and speech reception in
noise (Elliot & Katz, 1980; Erber, 1974; Goldman, Fristoe, & Woodcock, 1970; Jerger,
Lewis, Hawkins, & Jerger, 1980; Ross & Lerman, 1970; Tillman & Carhart, 1966).
Speech reception tests that mostly are used clinically include the Central Institute
for the Deaf W-22 (CID W-22) by Hirsh et al. (1952), Northwestern University Auditory
Test No. 6 (NU-6) by Tillman & Carhart (1966), Phonetically Balanced Kindergarten
Test (PBK-50) by Haskins (1949), and Northwestern University Children's Perception of
Speech (NU-CHIPS) by Elliot & Katz (1980). Common characteristics among these tests
are that they are based on monosyllabic frequently used words which are familiar to the
target population (at least at the time of test development) and were developed based on
measures of correct responses as a function of intensity of presentation. The articulation
function is a common representation of speech audibility result, which forms an ogive or
S-shaped curve and indicates the degree a person's hearing ability improves given
increases in the intensity of the material presented (Carhart, 1951). The shape of the
ogive and the 50% point (threshold point) are affected by the speech material presented
as well as the speaker. That is, different speech materials produce different ogives. If
different speakers recorded the same speech material, the result would yield different
ogives (Beattie, Edgerton, & Svihovec, 1977; Beattie, Svihovec, &Edgerton, 1975;
Carhart, 1965; Doyne & Steer, 1951; Hirsh, Reynolds, & Joseph, 1954; Kruel, Bell, &
Nixon, 1969; Wilson & Carter, 2001; Wilson & Oyler, 1997). Results from adult speech
recognition test (NU-6) and children's tests (PBK-50 and WIPI) were compared
(Sanderson-Leepa & Rintelman, 1976). The adult test was found to be inappropriate for
use with young children age 3.5 to 9.5 years while WIPI was most appropriate for young
children ages 3.5 to 5.5, and NU-6 was appropriate but more difficult than the PBK-50
for children age 7.5 to 11.5 the. That is, speech material needs to be age appropriate, and
words need to be familiar to the target group. Carhart (1965), and Kreul et al. (1969)
recommend the use of a limited set of recorded materials to establish normative measures
because different recordings by the same speaker can result in different articulation
Studies of speech perception in other languages (e.g., Arabic, Polish, Korean, and
Chinese) have followed Egen's (1948) and Hirsh et al. (1952) lead in developing speech
reception material (Alusi, Hinchcliffe, Ingham, Knight, & North, 1974; Zakrzewski,
Jassem, Pruszewicz, & Obrebowski, 1976; Ashoor & Prochazka, 1982; Ashoor &
Prochazka 1985; Harris, Kim, & Eggett, 2003a; Harris, Kim, & Eggett, 2003b; Nissen,
Harris, Jennings, Eggett, & Buck, 2005a; Nissen, Harris, Jennings, Eggett, & Buck,
2005b). Special attention was paid to the familiarity of words, the equivalence of word
lists, and in some cases the phonetic balance of word lists. The resulting lists have had
similar articulation functions to the English word lists in shape and slope. Thresholds of
speech reception and word recognition were more varied across languages.
Rationale and Purpose
Hearing Loss in Developing Countries
The management of hearing loss in developing countries and the USA differs
significantly. The prevalence and demographics of the hard of hearing population also
differs and thus imposes different needs for services. According to the World Health
Organization (WHO), the number of deaf and hard of hearing individuals in developing
countries is twice that in developed countries (Smith, 2001). According to the American
Academy of Audiology an average of 3 in every 1,000 healthy newborns in the United
States has severe sensorineural hearing loss. In Jordan 6 in every 1,000 healthy newborns
have hearing loss (Al-Masri, 2003). For comparison purposes, the WHO reported that 4
to 5 children under the age of 18 in every 1,000 children have sensorineural hearing loss
in the South-East-Asian Region (Smith, 2001). This number is reported to be inaccurate
due to the lack of epidemiology surveys in developing countries; the actual numbers are
projected to be double what has been reported (Smith, 2001). Globally, the majority of
children are living in developing counties, which indicates that the majority of children
with hearing loss live in those countries as well (Jauhiainen, 2001).
The WHO identified the major causes of hearing loss to be chronic otitis media,
genetics, maternal and perinatal problems, and ototoxicity (Smith, 2001). The rates of
complications due to otitis media are 1/1000 in developed countries and 60/1000 in
developing countries. The incidence of deaths due to ear infections complications are
1/100,000 in developed countries and 1 /100 in developing countries (WHO, 1998). In
spite of the higher prevalence of hearing loss in developing countries, services and
technology generally are limited or lacking, especially in rural areas (WHO, 1998;
Jauhiainen, 2001). The prevalence of Ear, Nose and Throat doctors ranges from 1/
30,000 to 1/150,000 in developed countries and 1/2,000,000 in the less developed
countries in Africa (excluding South Africa and Egypt) (WHO, 1998).
Early identification and intervention for hearing loss in developing countries are
need. At the same time there is a severe lack of equipment and highly trained
professionals to provide such services. In spite of this general statement, services in some
developing counties are more advanced than others, and some individuals have the means
to afford world class services (Jauhiainen, 2001). The focus of this study is on audiology
in Jordan, specifically speech audiometric measure for Jordanian Arabic speaking
Hearing Loss in Jordan
Pilot data from screenings conducted by the Middle East Hearing Association
(MEHA) suggest estimates of the hard of hearing and deaf population in Jordan to be
64,000, with 2,200 infants with severe hearing loss born yearly (Al-Masri, 2003). Some
institutions provide limited services, including the Speech and Hearing Clinic at the
University of Jordan, Al-Ahliyya Amman University, Holyland Institute for the Deaf,
King Hussein Medical Center, and some private otolaryngology and audiology clinics
and hearing aid providers. However, diagnostic and rehabilitative services and
professional training are seriously lacking. There are initiatives by the Jordanian
government, the royal family in particular, to develop such services. Under the patronage
of His Royal Highness, Prince Firas Raad, MEHA was established in 1998 in cooperation
with the Canadian International Scientific Exchange Program, and a new center was
opened recently to provide services to the hearing impaired and deaf population. Projects
being implemented through this organization include newborn hearing screening, genetic
hearing loss research, audiologic evaluation, hearing aid fitting, audiologic rehabilitation
and follow up for children. Equipment needed for such services is available and some
basic audiometric measures are being performed. An important part of evaluation, speech
perception audiometry, is missing from the diagnostic battery. Speech perception
materials suitable for testing Jordanian Arabic speaking children are unavailable. The
purpose of this study is to develop a speech perception test for Jordanian Arabic speaking
Present Condition of Arabic Speech Audiometry
Carhart (1951) emphasized the importance of using familiar words that are in the
listener's native language. Tests developed in Arabic have been recorded using Maroccan
(Messouak, 1956), Iraqi (Alusi, 1974), Egyptian (Soliman, 1976; Soliman, Abd El-Hady,
Saad, & Kolkaila, 1987; and Soliman, Fathallah, & El-Mahalawi, 1987), and Saudi
(Ashoor et al., 1982; and Ashoor et al. 1985) dialects. The question arises as to whether
the different dialects have an effect on speech reception. Although all Arabic countries
share one standard Arabic language that is taught formally in school in the form of the
written language (Altoma, 1969; Al-Kahtani, 1997; Fatihi, 2001; Ferguson, 1956)
dialectal differences exist. Studies conducted to measure the dialectal effect on speech
reception in Arabic could not be located. Thus, one may question the effect of the dialect
related to the words used in that dialect as well as the articulation and voice
characteristics of the speaker.
Recordings by different speakers result in different word recognition scores, and
the use of different speech material has the same effect (Beattie et al., 1977; Beattie et al.,
1975; Carhart, 1965; Doyne & Steer, 1951; Hirsh et al., 1954; Kruel et al., 1969; Wilson
& Carter, 2001; Wilson & Oyler, 1997). This suggests a need to develop speech
audiometric material based on one recording to ensure reliable results. Recordings
reported in other studies (e.g. Alusi et al., 1974; Ashoor & Prochazka, 1985) have not
been available for wide use. One intended outcome of this study is to make available a
high quality digital recording of speech material appropriate for use in testing word
recognition loss of Jordanian children. Audiologic testing in Jordan currently is based on
non-speech related audiometry, including pure-tone audiometry, otoacoustic emissions,
and auditory evoked potentials.
This study has two main goals. One is to develop four Jordanian Arabic 50-word
lists appropriate to use in word recognition measures for Jordanian children ages 6
through 9. Another goal is to investigate the effect of using words recorded in Saudi
dialect on word recognition abilities of Jordanian children who display normal hearing
and hearing loss.
1. Is there a significant difference in word recognition abilities of Jordanian Arabic
speaking children, given increases in intensity of presentation?
2. Can four parallel word lists be developed in Jordanian Arabic language (e.g., their
psychometric qualities of the word lists do not differ significantly)?
1. Do word recognition abilities of Jordanian Arabic speaking children differ when
listening to words presented in Jordanian versus Saudi dialects?
2. Do word recognition abilities of Jordanian Arabic speaking children who display
normal and hearing disabilities differ when hearing words presented in a Jordanian
versus Saudi dialect?
3. Does word recognition ability differ when hearing speakers using the same dialect?
1. An increase in presentation level will increase word recognition scores.
2. Four lists of 50-words with similar characteristics can be created to be used in
measures of word recognition abilities.
1. Jordanian children will produce higher word recognition scores when listening to
words presented in Jordanian dialect than when listening to words presented in Saudi
2. Jordanian and Saudi dialects will have the same effect on word recognition scores of
Jordanian children with normal hearing and with hearing loss.
3. Word recognition will differ for two talkers with the same Arabic dialect.
REVIEW OF THE LITERATURE
Importance of Speech Audiometry
Non-speech audiometric procedures provide valuable information needed for
description of hearing loss and estimation of amplification benefit. These tests, however,
cannot measure the effect of hearing loss on speech (Carhart, 1951; Cramer & Erber
1974; Davis, 1948; Doyne & Steer, 1951; Erber, 1974; Hirsh, et al. 1952).
Speech audiometry requires a language related test material, and thus may be
influenced by the phonetic, melodic, and intonational differences between languages
(Carhart, 1951). Different languages require speech tests that consider the features unique
to the language. Distinctive features of languages can result in different auditory
requirements and affect the auditory capacities evaluated. To provide comprehensive
audiologic services, it is necessary to use speech audiometry for measurement of hearing
and the outcome of management.
Speech perception is important in facilitating cognitive development and normal
language acquisition (Cramer & Erber 1974). Hearing loss is an obstacle of language and
speech acquisition. Pure-tone thresholds provide information about detection of sound at
specific frequencies, yet provide little information about the perception of complex
signals such as speech. Knowledge of a person's ability to perceive speech can provide
information as to the extent hard of hearing children are able to communicate effectively
and, in addition, how they are likely to learn language. Speech audiometry is a clinical
approach in which well-defined speech samples are presented using a calibrated system
to measure an important aspect of hearing ability. Measurement of speech perception
different intensities results in an articulation function taking the shape of an ogive or S-
shaped curve, that indicates the degree a person's hearing ability improves with an
increase of the intensity of the material presented (Carhart, 1951).
Over 50 years ago, Carhart (1951) reported on the importance of speech
audiometry in hearing assessment. He considered it to be the most useful contribution to
hearing testing after the introduction of pure-tone audiometry. Carhart emphasized the
value of well-defined speech audiometry to provide finer classification of hearing loss,
and its importance in providing measures for educational and rehabilitation purposes.
Another early hearing researcher, Hallowell Davis (1948), acknowledged that there is no
simple interpretation of the pure-tone audiogram to express the patient's ability to hear
speech. It is important to measure the patient's ability to discriminate speech just as it is
important to measure the hearing loss in decibels.
Fletcher noted, "The process of speaking and hearing are very intimately related, so
much so that I have often said that, we speak with our ears. We can listen without
speaking but we can not speak without listening" (p.Al Fletcher, 1995). Speech
perception and language acquisition are two closely related processes. Hearing loss
usually disrupts the process of speech perception and thus delays language acquisition.
Therefore, Cramer & Erber (1974) emphasized the need for an accurate and valid
measure of speech perception for hearing impaired children. Arlinger (2001) emphasized
the importance of using speech recognition to measure hearing aid benefit in children,
and considered periodic evaluation of hearing aid benefit of great importance for children
to monitor their language and speech development. Blamey, Sarant, Paatsch, Barry, Bow,
Wales, Wright, Psarros, Rattigan, & Tooher (2001) used a measure of speech perception
to evaluate benefit of amplification, and the relationship between speech perception and
speech production, language, hearing loss and age in 87 children age 4 to 12 years.
According to this study, the authors expected the language delay to be 4 to 5 years by the
time children are 12 years old. Speech perception scores are expected to improve
significantly with the improvement of language; specifically they expect children to score
90% on the open set Bench-Kowal-Bamford (BKB) sentence test when they reach the
level of language proficiency of a 7 year old. Oster (2002) examined the relation between
audiological measures and speech intelligibility for eleven profoundly deaf Swedish
teenagers (age 15 to 17 years). Their pure-tone averages (PTA) ranged from 90 to 108 dB
HL. Correlation analysis was assessed for the intelligibility of the children's speech and
their pure-tone average, the shape of the audiogram, and residual hearing use. Results
showed that there was great variation in their speech intelligibility in spite of the narrow
range of PTA, indicating that the speech intelligibility cannot be estimated based on PTA.
The correlation between speech recognition scores and speech intelligibility scores
resulted in a positive correlation of 0.73, confirming a high correlation between residual
hearing use and speech intelligibility. Oster (2002) concluded that a simple speech test
can be used as a predictor of prelingually deaf children's ability of developing intelligible
speech. Laukil & Fjermedal (1990) researched the reproducibility of bone conduction
thresholds and the speech recognition thresholds. The results show low variability and no
significant difference between the two measures making the speech recognition threshold
measure a reliable one for Norwegian Spondees. These studies indicate that speech
perception measures are very important and reliable clinical tools.
Speech Audiometric Tools
Speech audiometry is an important part of an audiologic evaluation battery
(Arlinger 2001; Carhart 1951; Cramer & Erber, 1974; Curry 1949; Davis 1948; Erber,
1974). There are two essential measures of speech audiometry: the measure of speech
recognition threshold (SRT) and the measure of speech recognition at suprathreshold
levels (Carhart 1951; Davis, 1948). Davis (1948) has identified these measures as two
dimensions of hearing loss; one is hearing loss (dB level) and the second is
discrimination loss (word recognition score). Discrimination loss relates to the loss of the
ability to recognize words even when they are made audible. Several tests have been
developed to measure these two dimensions of hearing loss. The most frequently used
ones for speech perception are speech recognition threshold (SRT) test and word
recognition scores (WRS). An example of the SRT test is the Central Institute for the
Deaf spondee word test (CID W-2) by Hirsh et al. (1952). Many word recognition
materials lists have been developed over the years including the Phonetically Balanced 50
word lists (PB-50) described by Egan (1948), the CID W-22 (Hirsh et al., 1952), the
Northwestern University Test No.6 (NU-6) by Wilson and Oyler (1997), the PB
Kindergarten word test (PBK) by Haskins (1949), the Word Intelligibility for Picture
Identification Test (WIPI) by Ross & Lerman (1970), and the Northwestern University
Children's Perception of Speech (NU-CHIPS) by Elliot & Katz (1980). Egan (1948)
specified the criteria of selecting word lists for the word recognition tests as follows:
1. Monosyllabic structured words
2. Equal average of difficulty between lists
3. Equal range of difficulty within lists
4. Equal phonetic composition between lists
5. A composition representative of spoken English
6. Commonly used words
The above listed speech perception tests were developed following Egan's
guidelines. Speech audiometric tests have been developed in many different languages,
each to fit the requirements of measuring speech perception based on the specific features
of that language.
Measurement of speech perception tests included the articulation function that
defines word recognition scores at different intensity levels. The measurement starts at a
very low intensity level where the material is unintelligible. As the signal intensity level
is increased, the listener is able to identify correctly more of the stimuli up to a point at
which the intensity is high enough for the listener listening to his native language to
identify all the material without error. Fletcher (1929) demonstrated that the shape of the
curve changes from one material to another and, using the same material, measurement of
speech recognition with different speakers results in different shaped curves. With speech
discrimination loss, the articulation curve not only shifts at the dB axis but it also changes
in shape. The curve reaches a plateau at percentage correct levels below 100% (Davis,
1948). Davis concludes that this effect results from the loss of sensitivity, especially in
the high frequencies, which is important for consonant recognition and clarity of speech.
The shape of the curve is again different for each hearing impaired individual. It might be
of normal shape and shifted to the right indicating higher intensity levels, or it might have
a different shape and not reach the 100% correct identification.
Pediatric Speech Perception Materials
Children's speech perception tests have been developed with different materials
and different tasks appropriate for different age and ability levels. Some tests are "open
set" (i.e. the listener has no knowledge of the category of word or any contextual cues)
and require verbal response (e.g. Phonetically Balanced Kindergarten 50 Word Test,
Haskins 1949), whereas others are "closed set" (i.e. the listener is provided with a set of
3-6 options to chose from or the category of words is specified to provide some cues)
requiring picture pointing (e.g., Word Intelligibility by Picture Identification, Ross &
Lerman, 1970). Following is a detailed description of the most commonly used children
speech perception tests (see table 2-1 for list).
Haskins (1949) developed The Phonetically Balanced Kindergarten 50 Word Test.
Though the Haskins lists appeared on in the author's masters' thesis at Northwestern
University, they have been widely used.The four lists were developed based on the
phonetically balanced word lists (PB-50) used by Egan (1948) for assessment of speech
perception in adults Haskins selected words that were among the 2500 words most used
by kindergarten children (The International Kindergarten Union, 1928). Measurement of
the psychometric function and the equivalence of wordlists were completed with adult
normal hearing participants with one randomization of lists presented at 5 intensity
levels. As a result, lists 1, 3, and 4 were found to be equivalent while list 2 was easier.
The slope of the psychometric function between 20 and 80% word correct was 4%/dB, as
reported in Mayer & Pisoni (1999). In spite of the wide use of these lists, no formal data
collection and analysis was completed with pediatric populations.
Other speech perception audiometric tests for children were mostly developed for
closed sets of words (Elliot & Katz, 1980; Erber, 1974; Erber, 1980; Goldman et al.,
1970; Jerger et al., 1980; Ross & Lerman, 1970), and sometimes with groups of hard of
hearing children as the only participants (Erber & Alencewicz, 1976; Ross & Lerman,
1970). Several closed set picture presentation tests are used regularly in audiology clinics.
These tests include the Word Intelligibility by Picture Identification (WIPI) by Ross and
Lerman (1970), the Northwestern University Children's Perception of Speech (NU-
CHIPS) by Elliott and Katz (1980), Goldman-Fristoe-Woodcock Test of Auditory
Discrimination (Goldman et al., 1970), and The Pediatric Speech Intelligibility test (PSI)
by Jerger et al. (1980). Erber & Alencewicz (1976) suggested a picture pointing closed
test to evaluate the word recognition ability of children with hearing loss that provides a
distinction of word recognition and word pattern recognition.
Adult Speech Perception Materials
Beattie & Warren (1983) described for adult word recognition tests in English, an
increase in intelligibility with the increase of intensity equivalent to 4.5%/dB in the range
of 20 to 80% scores, with an approximation of maximum intelligibility at level s of 25 dB
SL. Wilson, Zizz, Shanks, & Causey (1990) reported the NU-6 word recognition
threshold when spoken by a female speaker to be 4.5%, similar to other studies, while the
intensity level of 50% correct recognition was shifted 5dB to the right (higher than
previous studies). Wilson & Oyler (1997) compared the psychometric function of the
CID W-22 word lists and the Northwestern University No.6 (NU-6) as spoken by the
same talker and found the 50% score level to be at 15.6dB HL for the W-22 and 13.4 dB
HL for the NU-6. The slopes between 20% and 80% points were 4.8%/dB for the W-22
and 4.4%/dB for the NU-6. These results are comparable to speech audiometry data for
other languages For example, Harris et al. (2003a) studied the psychometric function of
wordlists spoken by males and females and found the mean 50% level in Korean to be at
11.4 dB HL for male speakers and 10.7dB HL for female speakers with mean slopes
between 20% and 80% points of 4.4%/dB for male and female speakers. Niessen et al.
(2005a) found a threshold level for Chinese Mandarin speech materials of 5.4 dB HL for
male speakers and 2.3dB HL for female speakers, and mean slopes between 20% and
80% points were 6.3%/dB for male speakers and 7.1% for female speakers. The
difference in the Chinese word lists might have been because disyllabic words were used
in the composition of the lists. Alusi et al. (1974) developed equivalent lists for speech
recognition in Arabic with a threshold level of 22.5 dB HL and a slope of 5%/dB.
In summary, studies of monosyllabic lists in three languages (English, Korean, and
Arabic) show similarity in psychometric function slopes (ranging from 4.4% to 5.1%)
whereas the study of disyllabic Chinese word lists showed steeper slopes (6.3% and
7.1%). Table 2-2 lists the thresholds and slopes of word lists in the different languages
listed above. Review of data displayed in Table 2-2 shows that there is a difference in
threshold among languages, with the Chinese disyllabic words having the lowest
threshold (2.3 dB HL) and the highest for Arabic monosyllabic word lists (22.5 dB HL).
Speech Reception Threshold Material
Studies of speech recognition thresholds (SRT) lists in different languages revealed
steeper slopes than those reported for word recognition lists. SRT measures in English are
composed of disyllabic spondees. Hirsh et al. (1952) reported a psychometric function
slope between 20% and 80% of 8%/dB. Young, Dudley, & Gunter (1982) reported a
slope of 10%/dB, and Wilson & Strouse (1999) reported a slope of 7.4%/dB. In studies of
languages other than English, such as the study of trisyllabic Chinese Mandarin materials,
Nissen et al. (2005b) found a slope between 20 and 80% of 9.7%/dB for a male speaker
and 10.5%/dB for a female speaker. According to Nissen (2005b), slopes of SRT tests in
other languages are at similar levels, i.e., Polish = 10.1%, Spanish = 11.1%, and Italian =
7.3%. Harris et al. (2003b) in their study of Korean disyllabic words found a slope of
10.3% for male speakers and 9% for female speakers. Ashoor & Prochazka (1982, 1985)
reported a slope of SRT word lists of 5% for both adults and children's wordlists. Slopes
of Ashoor's lists are less steep than those reported in other languages, but similar to
slopes of word lists used in Alusi's word recognition scores for adults. Siegenthaler,
Pearson, & Lezak (1954) investigated the speech reception threshold for children using
monosyllabic words and found the slope to be 8.6%/dB between 20% and 80% correct
word recognition. Ashoor & Prochazka found the threshold of word recognition to be at
2.2dB HL for adults (1982) and at 0 dB HL for children (1985). Table 2-3 lists speech
reception threshold wordlists in different languages.
Full List and Half List Use
The use of full lists or half lists depends on the patients' performance on the test.
Studies by Beattie and Warren (1983), Dubno, Lee, Klein, Matthews and Lam (1995),
and Thornton and Raffin (1978) investigating the confidence intervals of using full lists
of 50 words and half lists of 25 words in initial testing and retesting of patients'
performance on speech reception. Thornton and Raffin (1978) described the variability
in speech discrimination scores based on the CID W-22 test, and highlighted the
differences in variability between using full lists and half lists. Their results show the fact
that the closer the scores are to either end of the spectrum of scores (0 or 100) and the
more words are included in the list the less variability there is in scores and the smaller
the confidence interval. For example if a patient scored 96% on a 50 word list the
confidence interval is between 86-100. In other words if the patient scored 96% the first
time and was retested, a score between 86-100 will be considered not different from the
first score. While if the patient scored 96% on a half list of 25 words, the confidence
interval is between 80 and 100. With lower scores, closer to 50%, the confidence interval
grows larger more so for half lists than for full lists. This can be used as an indication for
the need to use a full list versus a half list. That is if the score is closer to 50%, the use of
50 word lists would provide a more accurate measure. In comparing the test retest
results from hearing impaired participants, Beattie and Warren (1983) found the standard
deviation in test retest results using 25 word lists to be 10%, which was reduced to 8%
using 50 word lists and to 6% when using 100 word lists. In Beattie et al.'s judgment, this
difference was not significant to increase the size of test material and was satisfied with
the 25-word list size.
Dubno et al. (1995) studied the correlation between the degree of hearing loss and
word recognition scores. They studied the word recognition scores from 407 ears with
normal hearing and mild to severe hearing loss, with the goal of providing data for
confidence limits of scores on 25 and 50 NU-6 word lists in relation to the PTA. The
authors provided tables of scores for 25 and 50 word lists corresponding to the 95%
confidence limit of best performance (PBmax). They found a correlation between word
recognition score and PTA, where a lower PTA resulted in higher scores. These findings
are intended to help in diagnosis decisions on whether the score is considered within
expected range for the degree of loss or whether it is poorer than expected and thus
requiring additional testing. Dubno et al. (1995) cautioned about the use of these tables to
generalize to other lists since different material would have different results. Still this
gives an indication for clinicians and researchers to be cautious when using word lists at
one presentation level and to keep this data in mind when making clinical decisions.
Arabic Speech Audiometry
Several speech perception tests are available in the Arab countries. These include
speech recognition tests for adults in Moroccan (Messouak, 1956), Iraqi (Alusi et al.,
1974), Egyptian (Soliman, 1976) dialects, and an SRT test (Ashoor & Prochazka, 1982)
in Saudi dialect. Pediatric speech audiometry tests have been developed including an
SRT test in Saudi (Ashoor & Prochazka, 1985) and Egyptian (Soliman et al., 1987b)
dialect, an Arabic word intelligibility (recognition) by picture identification in Egyptian
dialect (Soliman et al., 1987a), and an Arabic speech pattern contrast (ArSPAC) test
developed in Israel (Kishon-Rabin & Rosenhouse, 2000). Some of these publications are
inaccessible (Messouak, 1956; Soliman, 1976; Soliman et al., 1987a; Soliman et al.,
1987b). Insufficient details are found in the literature to allow for in depth discussions of
these tests. Recordings of the test materials are not widely available, even though the
word lists are printed in the publications (Alusi et al., 1974; Ashoor & Prochazka, 1985;
Kishon-Rabin & Rosenhouse, 2000). Allusi et al.'s (1974) and Ashoor & Prochazka's
(1982 and 1985) studies address word recognition comparable to the present study;
therefore these studies are discussed in detail.
Comparison of Arabic Speech Reception Tests
Alusi et al. (1974) and Ashoor & Prochazka (1982) used monosyllabic words in
their word lists. The structure of monosyllabic words was CVC, CVCC, CVVC, and
CVVCC, with all 28 consonants and 6 vowels of standard Arabic represented. Alusi et al.
(1974) divided the 150 monosyllabic words into 6 phonetically balanced lists of 25
words. Ashoor & Prochazka's adult lists (1982) contained 120 words divided into 6
phonetically balanced lists of 20 words, and their children's lists (1985) included 80
words divided into 8 lists of 10 words. Both Alusi and Ashoor based their phonetic
balance of lists on the frequency of consonant and vowel occurrence compared to
continuous text, based on counts conducted at the time of study. Ashoor & Prochazka
(1985) in his children's lists focused on equal distribution of syllable structure more than
the phonetic representation, while he kept the overall phonetic balance in all 80 words.
To ensure word familiarity, both researchers used standard Arabic, the main
teaching language in schools and universities as well as the language of mass media (i.e.,
newspaper, radio and TV broadcast). The word sources of choice were elementary school
books, children's stories, and daily newspapers. Absurd words and technical vocabulary
were excluded. In addition both researchers chose words that are similar in standard
Arabic and colloquial. Ashoor & Prochazka (1982 and 1985) ensured the word
familiarity by collecting ratings from a large number of participants coming from 14
different regions of Saudi Arabia.
Lists were recorded at voice intensity of 70 to 75dB SPL, fluctuation was limited to
+5 dB on volume meter in sound treated booths, and ambient noise did not exceed 30
dBA SPL. Neither researcher used carrier phrases. Alusi et al.,'s recording (1974) was in
standard Baghdad dialect while Ashoor and Prochazka's recordings (1982 and 1985)
were in standard Saudi dialect. The rates of recording varied. Alusi et al. (1974) recorded
8 words per minute, whereas Ashoor and Prochazka recorded 12 words per minute for
adults and 6 words per minute for children (Ashoor & Prochazka, 1982, and 1985).
The intelligibility tests were performed with somewhat different criterion. Alusi et
al. (1974) included 17 participants age 20 to 38 years representing several Arab countries,
since his goal was to develop a test that could be used in different Arab countries. Ashoor
& Prochazka (1982) enrolled 74 adult participants age 20 to 35 years representing 14
rural and urban areas in Saudi Arabia, and 100 children age 4 to 9 years representing
most Saudi dialect areas. Both studies (Alusi et al., 1974; and Ashoor & Prochazka,
1982) presented the stimulus words via headphones to their adult participants, while
Ashoor and Prochazka (1985) presented the stimulus words via sound field to their
pediatric participants. In all studies the authors attempted to measure the difference in
inter-list difficulty and also the difference associated with presentation method. The word
lists were presented at different intensity levels ranging from speech detection thresholds,
increasing by 5dB steps, up to the point where 100% of the words were identified
correctly. There was no difference between lists in terms of difficulty or the order of
stimuli presentation in ascending or descending dB level, within each study. Resulting
articulation function curves from all three studies were similar to those of other
languages, including English, in shape and slope. The findings suggest the lists are
suitable as speech recognition measure.
Ashoor & Prochazka (1982) found a slight difference between the adult groups of
students and non-students, i.e., a curve shift further to the right for the latter group
indicating higher threshold levels (in dB). In addition, he found a difference between two
age groups (4 to 5 years and 5 to 9 years). The younger group needed higher intensity
levels than the older group to reach threshold level of 50% word recognition, a finding
that may be related to maturation and knowledge of the language.
Use of Arabic Speech Reception Tests
Though Alusi et al. (1974) and Ashoor & Prochazka (1982 and 1985) recorded
their material, these recordings were not marketed for wide distribution. The limited
distribution may have several explanations. One is the small number of audiologists and
audiological services in Arab countries. For example, Saudi Arabia has one of the most
advanced services for communication disorders among the Arab countries. With a
population of 25 million, there are 14 registered audiologists at the Saudi Speech
Pathology and Audiology Association and five facilities that provide audiological
services (SSPAA, 2004). The second possible explanation for the limited distribution of
speech materials is the difference in dialects between Arab countries. Although Arab
countries share the standard written Arabic language, there is a wide range of dialects
(Fatihi, 2001). Published speech recognition tests are in Moroccan, Baghdadi, Egyptian
or Saudi standard dialect. The possibility of using one test across the Arab countries has
not been investigated. Alusi et al. (1974) has suggested the possibility of using the word
lists he developed in all Arab dialects since the words were taken from standard Arabic.
However, Alusi's speech materials were recorded in a Baghdad standard dialect. In
developing the speech test, Alusi had a limited number of participants (17) representing
"several" Arab countries (the author did not specify which countries), who were young
educated adults. The sample did not necessarily represent the large Arabic speaking
population). However, Alusi et al. did attempt to meet the criterion of word familiarity by
choosing words from children's books and newspapers in order to include educated and
un-educated populations. He did not describe a specific comparison between participants
from different countries to support his argument.
Dialectal Differences in Arabic
One of the goals in the present study is to determine whether there is a difference in
word recognition scores for normal hearing and hearing impaired children listening to
Jordanian and Saudi dialects. The issue of Arabic language diversity and its dialects is
important in the selection of speech material and speaker. Although a single standard
Arabic language is used in all Arab countries, dialectal differences do appear. The
difference between standard and dialect with the Arabic language, similar to other
languages, such as Greek, Swiss German, and Haitian Creole, is referred to as diglossia.
Furgeson (1959) defined diglossia as the presence of a stable situation of a language in
which there is a dialect of a language primarily used in daily communication and a very
different superimposed variety of the language that is part of a highly respected large
body of written literature or a previous period. The written literature is learned by formal
education and is used in formal speech (such as news and political speeches), but not in
every day conversation. All written materials (e.g., school books, news paper,
commercial material, official documents, and instructional materials) are written in
standard Arabic and in social settings people use the colloquial dialect. Thus children
learn the colloquial dialect first and the standard language is taught formally in
educational settings (Al-Kahtani, 1997; Altoma, 1969; Fatihi, 2001; Ferguson, 1956).
With the increase in mass media and early education, children nowadays are more
exposed to standard Arabic through television programs, radio and early reading
The differences between the two versions of Arabic are grammatical, phonetic, and
lexical in nature. Standard Arabic is considered syntactically more complex and richer in
lexicon. In spite of these differences, there are many similarities. Altoma (1969) found
that 83.5% of words in different colloquial dialects are shared with standard Arabic, as
well as the syllabic structures of words. In general standard Arabic is considered a more
prestigious language but there is no competition between the two versions of language
since each serves a different purpose and they are not totally interchangeable in use by
situation. In other words there are situations where standard Arabic is inappropriate and
visa versa (Al-Kahtani, 1997; Ferguson, 1959; Ibrahim, 2000).
Abd-el-Jawad (1987) and Al-Kahtani (1997) reported that educated Arabic
speakers frequently switch between standard and colloquial Arabic in a very natural
manner. Differences are to some extent between social groups, but these differences have
no effect on communication. Linguists have determined the status of a dialect based on
the characteristic of mutual intelligibility among dialects. Mutual intelligibility is based
on a scale of the physical proximity of the regions, that is, the closer the region the more
mutually intelligible, and the further apart the regions, the less mutually intelligible
(Fatihi, 2001). The differences and similarities between dialects and between standard
Arabic and dialects must be considered when developing speech perception test materials
and when choosing speakers. Several groups of researchers (Beattie, et al. 1975; Beattie,
et al. 1977; Carhart, 1965; Doyne & Steer, 1951; Hirsh, et al. 1954; Hood & Poole 1980;
Kruel, et al. 1969; Palmer, 1955; Wilson & Carter, 2001; Wilson & Oyler, 1997)
demonstrated significant differences in speech perception scores with different speakers,
regardless of gender or age, assuming the speech perception test material were recorded
by native speakers of English in standard dialects. In the case of Arabic, it can be argued
that there is no difference among the different Arab countries when using standard
Arabic. The question whether standard Arabic is a representative sample of the spoken
dialects remains unanswered.
Special Considerations for the Present Study
Speech perception differs significantly between age groups. Elliot (1979) found
that scores of children age 9 were poorer than older children on speech perception in
noise test (SPIN), while the older group (15 to 17 year olds) scores were comparable to
adult scores. Elliot, Connors, Kille, Levin, Ball & Katz (1979) found no significant
difference in scores for children age 5 to 8 years while 10 year olds performed at adult
levels (no 9 year old participants were included in this study). Schwartz and Goldman
(1974) assessed the performance of young children in nursery, kindergarten and first
grade and found significant differences between the three groups. In another study
Sanderson-Leepa & Rintelmann (1976) compared the speech performance of children
ages 3.5 to 11.5 on different speech perception tests and found no significant difference
in the 7.5 and 9.5 age groups on the WIPI and PBK-50 tests compared to the younger and
older groups. Ashoor & Prochazka (1985) found similar age differences for the Saudi
Arabic test when comparing scores for 4 to 5 year old children to scores for 6 to 9 year
old children'. Based on these results, participants aged 6 to 9 years were recruited in the
As mentioned above, studies by Elliot (1979), Elliot et al. (1979), Sanderson-Leepa
& Rintelmann (1976), Schwartz & Goldman (1974), and Goldman et al. (1970), show
that significant differences in word recognition scores are found for children younger
than 6 years and older than 9 years in comparison to children age 6 to 9 years. These
others reported no significant difference in word recognition scores between children
aged 6 through 9 years.
Individual differences between speakers can affect speech perception scores (Hood
& Poole 1980). Different speakers produce different articulation curves when using the
same words. Most words maintain their order of difficulty across speakers. Palmer (1955)
investigated the effect of gender on speech perception scores. He based his question on
the notion that hard of hearing individuals have an easier time hearing men's voice than
women's voice. In his study he used nine speakers; three male adults, three female adults,
and three female children. When he compared scores from each group for hard of hearing
and normal hearing participants at a fixed intensity level, no significant difference was
found across speakers. One goal in the present study is to investigate the possibility of a
difference in scores with different dialects. Following Palmer's methodology, in the
present study three Jordanian and three Saudi male speakers were selected to complete
the word recording.
Stimuli for Dialectal Differences
In the present study the researcher's goal was to investigate the possibility of
difference in scores with different dialects since. In this study, a +9 dB signal to noise
ratio was used to avoid ceiling effect in normal hearing children's performance and to
reduce the variability in scores. The choice of +9 dB signal to noise difference was based
on the Goldman et al. (1970) study of speech perception of children in quiet and in noise.
Goldman et al. (1970) observed a reduction in scores compared to the quiet condition
that started at -9 dB noise level. Resulting word recognition scores are expected to be less
than 100% correct. Schwartz & Goldman (1974) used the same level of signal to noise
ratio (+9 dB) to investigate the effect of different contexts and listening environments
(quiet and noise). They observed a significant increase in number of errors for all
contexts when noise was introduced. The effect of the smallest amount of noise was
clearly demonstrated in a study by Larson, Petersen, & Jacquot (1974) when they tested
the use of NU-6 word lists with children age 5.5 to 6.5 years of age. The presence of
noise at +20 dB S/N ratio had a significant effect on the children's performance
compared to adult performance under the same conditions. Keep in mind that for their
study Larson et al. have used adult material to test very young children. Based on the
above listed studies, a signal to noise level of+ 9 dB will be used for the present study.
Table 2-1 Children's speech reception tests in English.
Test Investigator Stimulus Respons Respons Target Published
e format e task population
PBK-50 Haskins 1949 Monosyll. Open set Verbal 6-9 years No
GFW Goldman, Monosyll. Closed Picture > 4 years Yes
Fristoe, & Words set pointing
Spondee Erber 1974 Spondee Closed Writing 8-16 years No
recognition words set
WIPI Ross & Monosyll. Closed Picture 3-6 years Yes
Lerman 1970 Words set pointing
BKB Bench, Koval, Sentences Open set Verbal 8-15 years No
PSI Jerger & Monosyll Closed Picture 3-10 years Yes
Jerger 1980 words and set pointing
sentences & verbal
NU- Elliott & Katz Monosyll. Closed Picture >2.5 years Yes
CHIPS 1980 Words set pointing
ANT Erber 1980 Numbers Closed Picture 3-8 years No
Table 2-2 Speech recognition word lists' mean dB HL levels at 50% and slopes.
Investigator Language Target Test dB @ 50% Slope
Wilson & Oyler English Adults CID 15.6 4.8
Wilson & Oyler English Adults NU-6 13.4 4.4
Harris et al. Korean Adults Male 11.4 5
Harris et al. Korean Adults Female 10.7 5.1
Nissen et al. Chinese Adults Male 5.4 7.3
2005a Mandarin speaker
Nissen et al. Chinese Adults Female 2.3 8.2
2005a Mandarin speaker
Alusi et al. 1974 Arabic Adults 22.5 5
Haskins 1949 English Children PBK-50 NA 4
Table 2-3 Speech reception threshold wordlists' mean threshold
between 20 and 80%.
dB HL levels and slopes
Investigator Language Target Test dB @ 50% Slope
Hirsh et al. 1952
Yourng et al.
Harris et al.
Harris et al.
Nissen et al.
Nissen et al.
This dissertation consists of two studies. In the first study, monosyllabic word
intelligibility was investigated as a function of presentation level in Jordanian dialect.
The following questions were addressed in the first study: Is there a significant difference
in word recognition ability of Jordanian children, given increases in intensity presentation
levels? Can four parallel word lists be developed (i.e. their psychometric qualities do not
differ)? The results of this study were used in the development of four parallel lists of
fifty monosyllabic words to be used as a speech perception test in Jordan.
The second study compared word recognition scores of Jordanian children listening
to words spoken in Jordanian and Saudi dialects. Specifically, the study was designed to
answer the following questions: Do word recognition abilities of Jordanian children differ
when listening to words presented in a Jordanian dialect compared to words presented in
a Saudi dialect? Do word recognition abilities of Jordanian children who display normal
hearing and hearing disabilities differ when hearing words presented in a Jordanian
dialect compared to words presented in a Saudi dialect? Does word recognition ability
differ when hearing speakers using the same dialect?
Inclusion criteria included age range and hearing sensitivity. Age range was
limited to children age six to nine years. Children displaying normal hearing sensitivity
and mild to moderately severe hearing loss were recruited. Hearing status was determined
using pure tone average thresholds of 500, 1000 and 2000 Hz. A pure tone average
threshold of 15 dB HL or less were considered normal hearing and thresholds greater
than 26 dB HL and less than 69 dB HL were considered within the range of mild to
moderately severe hearing loss. Tympanometry and pure tone audiometry were used to
determine hearing sensitivity fitting inclusion criteria. If the tympanogram showed
normal middle-ear function, the researcher proceeded to pure tone audiometry using
supra aural headphones (TDH-39). The participant was excluded if middle-ear function
was abnormal. If the pure tone average did not meet criteria, the testing was terminated.
In both cases results were explained to the parent if present. In case of hearing loss
detection or abnormal middle-ear function, the caregiver or adult accompanying the
participant was informed and briefly counseled on intervention.
Participants were recruited by word of mouth from the larger Amman area.
Participating children came to the clinic in Amman accompanied by their caregivers or an
adult with the parent's permission. Twenty normal hearing participants, ten males and
ten females, were included in the first study. A total of ten normal hearing, four males
and eight female, and ten hard of hearing, six male and four female, participated in the
second study. For detailed description of participant recruitment see Appendix A.
Speech materials were recorded by native Arabic speakers who were recruited
from the area of Provo, Utah, the location of Brigham Young University where the
recording took place. Potential speakers were initially personally contacted by phone or
by word of mouth. The purpose of the study and the speaker's role in the study were
explained. Five adult Jordanian males agreed to participate in the recording. Preliminary
5-minute recordings of continuous speech were made for the purpose of judgment of
dialect and clarity of speech. Five different adult Jordanian natives (three females and
two males) were asked to judge the speakers using two criterion, i.e., the dialect was a
general Jordanian dialect, and the ease of understanding the speaker's speech as rated on
a 10-point scale. The top ranked speaker completed the recording of the 304
monosyllabic words for study one. For the completion of recordings for study two the 3
top ranked Jordanian speakers were selected and agreed to participate in the recording.
Only three Saudi speakers agreed to participate in the recording for study two.
For the purpose of creating the 8 multi-speaker babble noise, four additional female
Arabic speakers were recruited. The female speakers included the primary investigator
and three females recruited form the Provo area by word of mouth. The recordings from
the 2 male Jordanian speakers who were not included in the final recording and 2 of the
speakers used for word recording randomly selected as well as the 4 female speakers
were used to compose the noise track.
All speakers have acquired Arabic in their home country. They were all students or
spouses of students who have come to the USA to peruse higher education degrees. All
speakers have been in the USA for no longer than 5 years.
Recording and Editing
The recording took place in an anechoic chamber at Brigham Young University
campus in Provo, Utah, USA. The chamber has a 0 dB SPL noise floor allowing for a
recording of 60 dB signal to noise ratio (S/N). The equipment used in the recording was
the same as reported by Harris et al. (2003a) and Nissen et al. (2005a). The recording
microphone used was a Larson-Davis model 2541, which has a flat frequency response
up to 20 kHz. The microphone was positioned approximately 6 inches from the speaker
at a 00 azimuth and covered by a 3 inch windscreen. The microphone signal was
amplified by a Larson-Davis model 900B microphone preamp, coupled to a Larson-Davis
model 2200C preamp power supply. The signal was digitized at a 44.1 KHz sampling
rate with 24-bit quantization by an Apogee AD-8000 analog-to-digital converter, with
preamp gain set to utilize the full range of the 24-bit analog-to-digital converter. The
digitized signal was stored on a hard drive for later editing. The speech material was
printed in large font on A4 paper and clipped to a clipboard that was suspended in front
of the speaker at a comfortable height to allow him to read the words facing the
microphone at 00 azimuth. The speaker was asked to say each word a minimum of four
times. A native judge (the primary investigator) rated each word for clarity and the best
production was selected for inclusion in the word recognition test. The intensity of each
selected word was edited using Sadie Disk Editor software (Studio Audio & Video Ltd.
2004) to produce a final recording with the same average root mean square (RMS) power
as the 1 kHz calibration tone in an attempt to equate the test word threshold audibility
(Harris et al. 2003a; Nissen et al. 2005a; and Wilson and Strouse, 1999). The recording
was converted from 24- to 16-bit quantization using the NS high dither option in the
Sadie Disk Editor software. The final word recording was digitally saved as wav files.
Each word was saved as a separate file. A total of 250 words were selected to be included
in the word recognition test; study one. For study two 33 monosyllabic words were
selected from Ashoor and Prochazka's (1985) speech reception threshold list for children.
The words were edited digitally to ensure same level of RMS as the 1 kHz tone.
The multi speaker babble material was completed using 4 of the male Jordanian
speakers and 4 Jordanian female speakers. The speakers were asked to read an article
taken form a Jordanian daily newspaper. The recording was completed following the
same procedure as for the word list. Three minutes of continuous speech was selected for
each of the 8 speakers. All silence between words and sentences was removed for each
speaker. Then the RMS level for each speaker was adjusted to have all 8 speakers at the
same level. Next the recordings were digitally mixed and run through a
compression/limiter to limit the range of peak variability. The resulting recording average
RMS was measured and adjusted to equal the level of the 1 kHz calibration tone. The 8
multi-speaker babble noise was saved in a separate wav file that would allow the play of
noise independently from the word lists.
Instrumentation for Data Collection
Data collection took place at the Middle East Hearing Association clinic in
Amman, Jordan in a sound treated booth designed locally for hearing testing purposes.
Pure tone and speech audiometry were conducted using an Interacoustics AC40
audiometer, connected to TDH-39 headphones. The audiometer was calibrated using a
Larson Davis System 824 sound level meter and a 6cc coupler. Calibration was based on
ANSI standards 2004. Measurements of sound levels at octave and half octave
frequencies met the ANSI standard 2004 with a deviation range of -0.6 to +0.3 dB. The
sound levels for speech through external input A and external input B were consistent
with ANSI standard 2004 with a deviation of-0.5 to +0.1 dB. Repeated measurements of
sound pressure level produced by the audiometer were within permissible ANSI tolerance
level of+ 3dB for frequencies of 500 to 4000Hz and +5dB for 6000 to 8000 Hz.
Special software was developed by Dr. Richard Harris at Brigham Young
University (2005) to control the playback of 1 kHz tone, noise and word lists from wav
files. The software also provides the documentation of data in an excel file spread sheet
with the following details: the date and time of presentation, participant assigned number,
participant gender, test ear, speaker gender, intensity level, signal to noise ratio, list
name, time of recording per list, wav file, word (in this case in Arabic), and the score.
Prior to data collection, the VU meter was adjusted to 0 VU using 1 KHz tone. For
the purpose of consistency in speech audiometry data collection, the left headphone was
randomly selected to always be used over the test ear. This step was taken to reduce
variability in the sound level presented.
Speech material was selected following Egan's (1948) criterion of monosyllabic
words, representative of spoken Arabic and commonly used words. A total of 304
monosyllabic words were selected for initial recording. Words that have similar forms in
colloquial and standard Arabic were selected. Children and teachers of first through third
grade rated these words as familiar. Rating took place at four elementary schools in
Amman Jordan. The words were read aloud to the children, they were asked to raise their
hand if they knew the meaning of the word and were able to use it in a meaningful
sentence. To ensure the accuracy of their response, the primary investigator (Nadia
Abdulhaq) started with three trial words that were familiar to children (window, door,
and table), randomly asked children to put words in a sentence, in addition unfamiliar
words (from old classical text) and nonsense words were included. For more detailed
description of word selection see Appendix B.
The primary investigator judged the recorded 304 words for clarity of recording
and in conjunction with familiarity ratings selected 250 words to be used in data
collection. The 250 words were divided randomly into two sets of ten lists of 25 words.
To allow the presentation using the software developed by Dr. Harris, a text file was
created for each list Al to A10 and B to B10 for each set of randomization respectively.
Lists were presented in counterbalanced order. Lists Al through A10 were presented to
participants 1 through 10 and lists BI through B10 were presented to participants 11
through 20. See Table 3-1 for order of presentation and level of presentation. The signal
was routed to the subjects through the Interacoustics AC40 audiometer, via the TDH-39
headphones. Speech material was presented at 10 different intensity levels starting at 0
dB HL and increasing by 5 dB increments up to 45 dB HL; to include a range of low to
high word recognition scores.
Procedure for Data Collection
Each participant was given the following general instructions:
"You will hear some words through the headphones, your will repeat the words you
hear. For example, if you hear the word /kitab/ (book), you would say?" The researcher
would wait for the participant to repeat the word. "That is good. And if you hear the word
/daftar/ (copy book) you would say? and the word /madrasa/ (school) you would say?"
Once the participant has followed instructions further details were provided:
"The words will be spoken very softly; you might or might not hear them at first.
That is OK. If you hear a word, repeat it. If you don't, just wait to hear the next one. The
words will get louder and louder."
The earphone was placed over the test ear. Two lapel microphones were attached to
the participant's shirt, one for talk back and the second for audio input of the audio-visual
recording. A video camera was set in front of the participant to obtain video recording.
The video camera was adjusted in height and angle to provide the best view of the
After the participant repeated a word, the investigator entered 1 for a correct
repetition and 0 for an incorrect repetition. If the participants did not repeat the word, the
investigator periodically used the talk forward microphone to ask the participant if he/she
had heard the word, and to encourage him/her to repeat whatever they heard. The
investigator waited 3to 5 seconds before recording an incorrect score in the case of no
repetition. After each list presentation, the presentation level was increased by 5dB.
Participants were encouraged between lists using verbal praise ("good job", or "you are
doing really well"). Most participants did not need a break or rest period throughout the
data collection procedure. All data for each participant, including presentation order and
scoring details, were saved in excel spreadsheets.
The dependent variable, defined as correct word recognition, was saved in binary
format (correct versus incorrect). The independent variables were different presentation
intensity levels and different word lists. The raw data were used in logistic regression
analysis. The logistic regression analysis provides derived variables that are key
parameters for the regression line that may be tested and compared between conditions
(in this study, the word lists). The raw scores of each word in the four lists were used in
the logistic regression analysis to calculate the logistic parameters for each list and half
list. See Appendix C for details on logistic regression analysis.
Recall that this study was designed to test the following hypotheses: 1. Increased
presentation sound intensity level will improve word recognition ability. 2. It is possible
to create four word lists that are parallel in function and can be used interchangeably to
test Jordanian children's word recognition ability.
The speech material consists of 33 monosyllabic words from Ashoor's (1985)
children's list. Ashoor's list was used as source of material because it was developed for
the purpose of Arabic speech perception specifically for children. The selection of the 33
words was based on two criteria: monosyllabic words and familiar to Jordanian children.
Recall that Ashoor's list consisted of a mixture of mono- and di-syllabic words, familiar
to Jordanian Arabic speaking children. The familiarity of words to Jordanian Arabic
speaking children was established through ratings provided by children in 1st through 3rd
grades (Abdulhaq, unpublished). See Appendix D for the list of 33 words and their
meaning in English.
A total of six recordings, each recorded by a different speaker (three Jordanian and
three Saudi speakers), were presented to all participants in counterbalanced order at
constant intensity levels (see table 3-2 for details). The speech material was presented by
playing wav files from a standard desktop PC connected to external inputs of the
audiometer using the same software as described in study one.
The words were presented to the hard of hearing participants at an audible level of
40 dB SL (re: the participant's pure tone average). For normal hearing participants, the
multi-speaker babble track was presented then the word list was presented. Words were
presented at 50 dB HL and the multi-speaker babble noise at 41 dB HL, i.e., with a signal
to noise ratio (SNR) of +9 dB.
For the purpose of statistical analysis of the data in these studies, the dependent
variable was defined as the correct word recognition score, and the independent variables
were defined as dialect, speakers, and hearing status. Raw scores were used in mixed
ANOVA to determine difference between speakers and dialects. To determine the effect
of dialect on normal hearing and hard of hearing children's scores a paired t-test of
dialects based on scores from each group separately was used. To determine the
significance of differences between speakers within a dialect paired t-tests of speakers
were used based on scores from all participants.
Table 3-1 Order of list presentation by participant and level of presentation
Participant Presentation dB HL level
0 5 10 15 20 25 30 35 40 45
1 Al A2 A3 A4 A5 A6 A7 A8 A9 A10
2 A2 A3 A4 A5 A6 A7 A8 A9 A10 Al
3 A3 A4 A5 A6 A7 A8 A9 A10 Al A2
4 A4 A5 A6 A7 A8 A9 A10 Al A2 A3
5 A5 A6 A7 A8 A9 A10 Al A2 A3 A4
6 A6 A7 A8 A9 A10 Al A2 A3 A4 A5
7 A7 A8 A9 A10 Al A2 A3 A4 A5 A6
8 A8 A9 A10 Al A2 A3 A4 A5 A6 A7
9 A9 A10 Al A2 A3 A4 A5 A6 A7 A8
10 A10 Al A2 A3 A4 A5 A6 A7 A8 A9
11 Bl B2 B3 B4 B5 B6 B7 B8 B9 B10
12 B2 B3 B4 B5 B6 B7 B8 B9 B10 B1
13 B3 B4 B5 B6 B7 B8 B9 B10 Bl B2
14 B4 B5 B6 B7 B8 B9 B10 Bl B2 B3
15 B5 B6 B7 B8 B9 B10 Bl B2 B3 B4
16 B6 B7 B8 B9 B10 Bl B2 B3 B4 B5
17 B7 B8 B9 B10 Bl B2 B3 B4 B5 B6
18 B8 B9 B10 Bl B2 B3 B4 B5 B6 B7
19 B9 B10 Bl B2 B3 B4 B5 B6 B7 B8
20 B10 Bl B2 B3 B4 B5 B6 B7 B8 B9
Table 3-2 Word list and dialect presentation order.
Participants Present. order
1st 2nd 3rd 4th 5th 6th
1 J1 S1 J2 S2 J3 S3
2 Si J1 J3 S3 S2 J2
3 S2 J2 J1 S1 S3 J3
4 J2 S2 S3 3 J1 S1
5 S3 J3 S1 J1 J2 S2
6 J3 S3 S2 J2 S1 J1
7 J1 S1 J2 S2 J3 S3
8 S1 J1 J3 S3 S2 J2
9 S2 J2 J1 S1 S3 J3
10 J2 S2 S3 J3 J1 S1
J = Jordanian speaker, S = Saudi speaker. The same order of presentation was
used for the normal hearing and hard of hearing group.
Twenty individuals participated in this study. Pure tone thresholds average (PTA)
indicated that all participants fit the criteria of a PTA of 15dB HL or better with a mean
PTA of 7.8 dB HL (standard deviation = 2.8, range = 3.3 to 12.5 dB HL). The better ear
was selected based on PTA or at random when both ears had the same PTA. Ten
participants were tested on the right ear and 10 were tested on the left ear. Average pure
tone thresholds for both ears of all participants are shown in Figure 4-1. For detailed
description of participants see Table 4-1.
Most participants completed the 250 words in a single session of 20 and 29 minutes
without a break other than the time it took to load each new list and adjust the intensity of
presentation. The pacing of the test depended on how quickly participants responded; the
program required the examiner to record the response before the next word could be
The raw score data of all participants at the 10 intensity levels were compiled for
each of the 250 words. The total of responses resulted in a score of correct recognition
out of 20 possible correct score per word. Words then were reordered by difficulty from
easiest to most difficult based on the total score (with lower scores indicating higher
difficulty). Four lists of 50 words each were created using the top 200 words ranked by
difficulty, as follows: Words that were tied in difficulty ranking were randomized prior to
sorting into lists. To help ensure range of difficulty between lists, the four most difficult
words were assigned randomly to four lists. The next four most difficult words again
were assigned randomly to the four lists and so on until a total of 50 words were included
in each list. The lists were labeled 1, 2, 3, or 4.
Within each list, the word's level of difficulty was used to assign it in order to
create two half lists of 25 words from each original 50-item list with the goal of having
an equal range of difficulty on both half lists. The half lists were labeled 1A and 1B (the
two lists taken from list 1) through 4A and 4B (the two lists taken from list 4). This
procedure used, to distribute words into lists, has been successful in developing difficulty
equivalent word lists in studies by Harris et al. (2003a and 2003b) and Nissen et al.
(2005a and 2005b). The eight 25-Arabic monosyllabic word half lists are presented in
Tables 4-2. The words are written in Arabic with their phonetic transcription, please see
appendix E for the English translations of the words.
Word recognition scores increased with the increase of intensity. A scatter plot of
the percent correct of responses, for the half lists (25 words) as a function of stimulus
level, is presented in Figure 4-1. Scores at the lowest intensity level of 0 dB HL ranged
from 0 to 2% while scores at the highest intensity level of 45dB HL ranged from 94 to
96%. Chi-square analysis (X2) of the half lists showed a highly significant effect of
intensity (X2 = 1,134.18, p <0.0001). Statistical analysis of data confirmed, as expected,
that scores at different intensity levels are not equal.
Differences between lists (X2 (3) = 1.67, p =0.64) and between half lists (X 2 (7) =
8.25, p = 0.31) were not significant. The mean slope of full lists at the 50% point was
4.44%/dB and ranged from 4.21%/dB to 4.63%/dB (range = 0.42%/dB). The mean slope
at the 20-80% range was 3.84%/dB ranging from 3.65 to 4.01%/dB (range = 0.36%/dB).
The mean threshold was 21.25 dB, and ranged from 21.21 dB to 21.32 dB (range = 0.107
dB). For the half lists, the mean slope at 50% was 4.47%/dB and ranged from 3.86 to
5.36%/dB (range = 1.5%/dB). The mean slope at the 20-80% range was 3.87%/dB with a
range from 3.34 to 4.64%/dB (range =1.30%/dB). Mean threshold was 21.25 dB, and
ranged from 21.04 dB to 21.38 dB (range = 0.34 dB). Table 4-3 and Table 4-4 include the
detailed results of the logistic regression parameters, the slopes at the 50% and 20-80%
levels, threshold intensity level, as well as threshold deviation from mean threshold, for
the lists and half lists respectively. Figure 4-3 shows the psychometric function for all
four lists and Figure 4-4 shows the psychometric function for the eight half lists. By
visual inspection, the curves are almost identical.
The intensity levels at the threshold point had a narrow range around the mean
value, from -0.03 to 0.11 dB for the full lists and from -0.21 to 0.13 dB for the half lists.
The difference between the calculated threshold and the mean threshold for each list and
half list was used to adjust the intensity of each list and half list digitally to achieve a
better equivalence between lists and half lists. The percent correct was recalculated based
on these adjustments for all lists and half lists and an adjusted logistic regression curve
was created. Figure 4-5 shows the difference between the unadjusted and adjusted curves.
The difference between the unadjusted and adjusted curves was small and barely
Ten normal hearing and 10 hard of hearing individuals participated in this study.
Pure tone threshold average measures indicated that all participants fit the criteria of a
PTA of 15dB HL or better with mean PTA of 6.3 dB HL (SD = 2.8, minimum = 1.7dB
HL and maximum = 10dB HL). The better ear was selected based on PTA or at random
when both ears had the same PTA; as a result 6 participants were tested on the right ear
and 4 were tested on the left ear. Figure 4-6 shows the average of pure tone thresholds of
normal hearing participants, for detailed description see Table 4-5.
Pure tone threshold average measures indicated that all ten hard of hearing
participants had a mild to moderately severe hearing loss with a mean PTA of 50.2 dB
HL (SD = 11.8, minimum = 31.7dB HL and a maximum = 63.3dB HL). All but two
participants had sensorineural hearing loss (SNHL). One participant (#2 in table 4-6) had
chronic conductive hearing loss and one participant (#7 in table 4-6) had a mixed loss.
Again the better ear was selected based on PTA or at random when both ears had the
same PTA; resulting in 5 right and 5 left ears being used. Figure 4-7 shows the average of
pure tone thresholds of hard of hearing participants, for detailed description see Table 4-
Most participants completed all six lists of 33 words in a range of 13 tol8 minutes,
and did not require a break in data collection. The pacing of data collected depended on
how quickly participants responded; the program required the examiner to record the
response before the next word could be presented. After data collection was completed
the score for each participant per speaker was calculated into percent correct. See Table
4-7 for detailed results. These data were analyzed to evaluate the hypotheses.
Mixed analysis of variance (ANOVA) indicated a significant difference between
dialects and speakers. Scores of all participants for all speakers were included in a mixed
ANOVA of speaker and dialect. In summary, analysis showed a significant difference
between dialects (F= 8.865, p = 0.008), significant differences among speakers (F =
6.181; p = 0.005), and a significant difference between speakers within each dialect (F =
5.153; p = 0.011). For detailed results see Table 4-8.
Paired sample t-test analysis indicated significant difference between dialects for
the normal hearing group, but no significant difference for the hard of hearing group. The
scores for each participant were averaged by dialect, and the averages were used in paired
sample t-test for each group separately (hard of hearing and normal hearing). Difference
between dialects for the normal hearing participant group were significant (t-test = -2.923
p = 0.017). Differences between dialects for the hard of hearing participant group were
not significant (t-test = -1.327 p = 0.217). Thus, hearing loss seemed to outweigh
dialectal difference. See Table 4-9 for detailed results.
Within each dialect one speaker was significantly different than the other two
speakers. A paired sample t-test was performed comparing results of speakers within
each dialect for all participants. Results indicated that scores associated with speaker J1
(in the Jordanian dialect) were higher than those for J2 and J3 (t = 4.203 and 3.802,
respectively; p = 0.0001 and 0.001). Paired scores associated with speakers J2 and J3
were not different (t-test -0.496, p= 0.625). For the Saudi dialect, scores associated with
speaker S2 were significantly lower than those for S1 and S3 (t-test = 2.658 and -2.183
respectively and p = 0.016 and 0.042). Differences between speakers S1 and S3 were not
significant (t-test = 0.047 and p = 0.962). See Table 4-10 for detailed results.
Frequency in Hz
250 500 1000 2000 4000 800(
Figure 4-1 Average pure tone thresholds at all frequencies for all 20 normal hearing
participants at 250, 500, 1000, 2000, 4000, and 8000 Hz.
Half List Raw Scores
2 4 6 8 10 12
14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
PresentationLevel (dB HL)
Figure 4-2 Half lists raw data scatter plot
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
Figure 4-3 Psychometric function of 4 lists of 50 words based on calculated percent
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
Figure 4-4 Psychometric function of 8 half lists of 25 words based on calculated percent
Lists 1-4 Adjusted Half-lists 1A-4B Adjusted
0 10 20 30 40 50 0 10 20 30 40 50
Presentation Level (dB HL)
Figure 4-5 Arabic monosyllabic psychometric functions for lists 1-4 (left) and half-lists
1A-4B (right). The top two figures depict unadjusted psychometric functions
and the bottom two figures depict psychometric functions adjusted for equal
performance at 50% correct recognition.
Figure 4-6 Average of pure tone thresholds of normal hearing participants at 250, 500,
1000, 2000, 4000, and 8000 Hz.
Figure 4-7 Average of pure tone thresholds of hard of hearing participants at 250, 500,
1000, 2000, 4000, and 8000 Hz.
Frequency in Hz
250 500 1000 2000 4000 8000
. 50 Right
Frequancy in Hz
250 500 1000 2000 4000 8000
Table 4-1 Normal Hearing participants' age, gender, test ear and PTA
Participant Gender Age Test ear PTA
4 F 6 Left 6.7
8 F 6 Right 10.8
10 F 6 Right 5.0
5 F 7 Left 9.2
7 F 7 Left 9.2
12 M 7 Right 3.3
15 M 7 Right 7.5
16 M 7 Right 5.8
19 F 7 Right 5.8
20 M 7 Right 6.7
6 F 8 Right 12.5
11 M 8 Right 4.2
1 M 9 Left 6.7
2 M 9 Left 4.2
3 F 9 Right 9.2
9 F 9 Left 9.2
13 M 9 Left 10.8
14 M 9 Left 6.7
17 M 9 Left 11.7
18 F 9 Left 11.7
Mean 7.8 7.8
Standard Deviation 1.2 2.8
Range 3.0 9.2
Minimum 6.0 3.3
Maximum 9.0 12.5
Table 4-2 Half word lists and transcription in IPA.
1A 2A 3A 4A
khair rab mars Sams
S ku:2 a ha:3 %e xeil U ta:3
i bs:t zir L 3in L ti:n
i ra:s i laf JT ha:l jj bi:r
j su:? ,2 na:r Y rad tu:b
lo:z Sad ji 1 tu:1 taht
JL xa:l j1 Ya:z bank L aks
S ka:s ts:r hu:t L sa:m
S bard kum ri:h 1. 3eib
Wi ?ahl L hur su:s t ku:x
b ba:s sad L ein hab
S said Xo:f 3a:f kalb
S mo:t L u:d sath si:x
S zo:3 L iz Z- ru:h ad
su:d sab raf Th o:d
S varb ma3d L SaXs zi:r
eo:b S a:y bur3 ramz
|J9 wa:d S :x bar sasb
j) waz si:n bint bu~d
l eal3 jI da:r taqs dars
ia fi: 1 du:d ?i ard mur
ei ? ScaTr ward deif
zeid e qalb ii anf Li alf
Sat_ ri:S S haq sabt
S so:t y di:n e hub bat
Table 4-2 continued
1B 2B 3B 4B
xas fard beid 3id
3ad eo:m eo:r xeit
kaf bs:9 mo:z il
nus i xalf taxt hal
lams 3SIS i:d 2 ism
nafs T a:m harb arS
Sum zay Ja qalb self
ru:s ha3 J za:d j0 fu:l
S bu:q ssir Sar 3a:r
f: lo:h ss:r lIs fa:s
S Yard 3a:d i ab Sa:S
jS o:k jwm ha:r darb
har ruz o Yo:s karS
c tu:t 3 gaw xat -3 su:r
SSak 3u:9 T nu:r eule
ze:t U ri:h qo:s fa:r
S di:k J, ma:l daw dam
S do:r S ab u na:s 2uxt
hizb i ax dub had
bo:t ri:m lub no:m
2i uns xa:s t xo: hi:n
haj saf ham lo:n
j qird S uS Sarq fard
kanz su:f duS ki:s
U S:b mad T am 3 ild
Table 4-3 Logistic regression results for the full lists, the calculated slopes at the 50% and
the 20-80% levels, the threshold dB level, and difference of threshold levels
from the mean threshold
Table 4-4 Logistic regression results for the half lists, the calculated slopes at the 50%
and the 20-80% levels, the threshold dB level, and difference of threshold
levels from the mean threshold
Slope at Slope from Threshold dB
Half Lists a b 50% 20-80% dB difference
1A 3.748 -0.177 4.426 3.832 21.170 -0.077
1B 4.104 -0.193 4.821 4.173 21.281 0.034
2A 3.969 -0.186 4.642 4.018 21.379 0.132
2B 3.249 -0.154 3.860 3.341 21.040 -0.206
3A 3.663 -0.171 4.285 3.709 21.370 0.123
3B 3.663 -0.172 4.305 3.726 21.272 0.025
4A 3.498 -0.164 4.093 3.543 21.363 0.117
4B 4.522 -0.214 5.361 4.640 21.087 -0.159
M 3.802 -0.1790 4.47 3.87 21.25 0.00
Minimum 3.249 -0.2144 3.86 3.34 21.04 -0.21
Maximum 4.522 -0.1544 5.36 4.64 21.38 0.13
Range 1.273 0.0600 1.50 1.30 0.34 0.34
SD 0.392 0.0187 0.47 0.40 0.13 0.13
Table 4-5 Selected characteristics of normal hearing participants
Table 4-6 Selected characteristics of hard of hearing participants
Table 4-7 Word recognition scores in percent correct for each subject per talker-talker
and the average of scores by dialect.
Group cipant J1 J2 J3 S1 S2 S3 Jordanian Saudi
1 1 0.94 0.91 0.97 0.94 1.00 0.97 0.94 0.97
1 2 0.94 0.94 0.97 0.85 0.88 0.97 0.95 0.90
1 8 0.58 0.30 0.42 0.45 0.24 0.42 0.43 0.37
1 15 0.82 0.76 0.67 0.82 0.73 0.61 0.75 0.72
1 16 0.70 0.70 0.45 0.61 0.61 0.55 0.62 0.59
1 17 0.70 0.52 0.67 0.70 0.61 0.73 0.63 0.68
1 18 0.58 0.64 0.52 0.79 0.79 0.64 0.58 0.74
1 19 0.70 0.48 0.82 0.76 0.61 0.82 0.67 0.73
1 23 0.97 0.64 0.85 0.85 0.94 0.94 0.82 0.91
1 7 0.67 0.39 0.67 0.70 0.52 0.76 0.58 0.66
2 6 0.94 0.55 0.76 0.82 0.73 0.85 0.75 0.80
2 5 0.82 0.73 0.82 0.79 0.73 0.79 0.79 0.77
2 4 0.85 0.61 0.79 0.67 0.79 0.97 0.75 0.81
2 3 0.70 0.64 0.64 0.79 0.82 0.88 0.66 0.83
2 10 0.88 0.79 0.58 0.91 0.67 0.52 0.75 0.70
2 11 0.79 0.82 0.42 0.85 0.67 0.82 0.68 0.78
2 22 0.82 0.70 0.55 0.88 0.85 0.76 0.69 0.83
2 14 0.94 0.97 0.88 0.97 0.91 0.97 0.93 0.95
2 20 0.82 0.58 0.73 0.88 0.64 0.91 0.71 0.81
2 21 0.85 0.82 0.73 0.82 0.82 0.94 0.80 0.86
Group 1 = hard of hearing, Group 2= normal hearing, J1= Jordanian talker 1, J2= Jordanian talker
2, J3= Jordanian talker 3, S1= Saudi talker 1, S2= Saudi talker 2, S3 = Saudi talker3
Table 4-8 Mixed ANOVA results comparing dialects for all participants' scores
df Mean Square F-test Sig.
Dialect 1 0.066 8.865 0.008
Talkers 2 0.091 6.181 0.005
Talker within dialect 2 0.027 5.153 0.011
Table 4-9 Paired Sample T-tests comparing dialectal difference in the hard of hearing
group and normal hearing group.
Group df T-test Sig. (2-tailed)
Hard of Hearing 9 -1.327 0.217
Normal Hearing 9 -2.923 0.017
Table 4-10 Paired Sample T-test results including all participants' scores
Pairs df T-test Sig. (2-tailed)
J1-J2 19 4.203 0.0001
J1-J3 19 3.802 0.001
J2-J3 19 -0.496 0.625
S1-S2 19 2.658 0.016
S1-S3 19 0.047 0.963
S2-S3 19 -2.183 0.042
The primary goal of the present study was to create four equivalent lists of words
for clinical assessment of speech perception in Jordanian Arabic speaking children. There
are no published word lists developed for the specific purpose of clinical speech
audiometry (word recognition) for Jordanian Arabic speaking populations. Although
other speech recognition materials have been developed and published over the past 3
decades, no study has evaluated the feasibility of applying Arabic speech audiometry
materials among different countries or different Arabic language dialects (e.g., the use of
Egyptian Arabic word lists in Jordan). In addition, recorded materials are not widely
distributed and, thus, not commonly used in audiology clinics within Arabic countries.
Four lists of 50 words were developed in the present study. The lists were not
significantly different in their psychometric functions. All had the same shape of curve
and very similar slopes (mean 4.4 %/dB, S.D. 0.215) and threshold points (mean 21.25
dB, S.D. 0.05), properties that are appropriate for clinical use as speech recognition
measures. The word lists developed in the present study were comparable in slope and
curve shape to word lists published in English (Haskins, 1949; Wilson & Oyler, 1997),
Korean (Harris et al. 2003a), and other words lists published in Arabic (Alusi et al. 1974).
The point of threshold (50% correct word recognition) was comparable to the Arabic
adult lists reported by Alusi et al. (1974). The point of threshold for the Arabic word lists
in the present study differed from other languages, suggesting the likelihood of a
language specific feature. The range of thresholds for the word recognition materials in
the present study (from 2.3 to 22.5 dB HL), confirm the appropriateness of the materials
as measures of word recognition ability.
There was no attempt in the present study to develop words lists that were
phonetically balanced. Carhart (1965) stated that the phonetically balanced CNC lists and
W-22 lists approximated the phonetic balance of every day spoken English but cannot be
true representation of everyday phonetic balance. The effect of familiarity of words on
speech perception performance is greater than the phonetic balance of the word lists.
Owens (1961) studied the effect of word familiarity on word recognition. He found that
listeners were more likely to make errors on less familiar words and, when they made the
errors, the listener's response was more likely to be a familiar word. Martin (2000) stated
that phonetic balance is not the only, or the main, factor in word list equivalence.
Therefore, an objective of the present study was to utilize familiar words with a balanced
range of difficulty between lists. In the present study, the psychometric curves of the full
lists and half lists were very similar. Statistical analyses of the full lists (X2 (3) = 1.67, p
=0.64) and half lists (X 2 (7) = 8.25, p = 0.31) showed no significant difference between
word lists and half lists despite the fact that no effort was made to account for phonetic
In comparison to the speech materials reported previously (Alusi et al., 1974;
Ashoor & Prochazka, 1982, and 1985) the recordings developed in the present study can
easily be applied clinically by audiologists in Arabic-speaking patient populations with a
CD player and distributed to audiologists throughout the Arab countries. However, the
issue of dialectal differences requires further investigation. Specifically, the possible
effect of dialect on word recognition performance should be assesses for children in
different Arabic speaking countries.
It was hypothesized that participants in this study would perform better when
listening to words spoken in Jordanian versus Saudi dialect and, in addition, that the
effect would be similar for both normal hearing and hard of hearing children. Dialect
appeared to exert an effect on word recognition ability of normal hearing Jordanian
Arabic speaking children. Results reported herein confirmed that word recognition scores
were higher when normal hearing children listened to words spoken in Saudi dialect. In
contrast, no significant effect for Jordanian versus Saudi dialect was found in word
recognition ability of children with mild to moderately severe hearing loss. In general,
word recognition performance was lower and more variable among hard of hearing
children than for normal hearing children, independent of dialect. However, the higher
word recognition scores for the Saudi dialect were persistent in both hard of hearing and
normal hearing groups. Perhaps because each list was limited to 33 words that were
highly familiar to the target population (i.e., Jordanian Arabic speaking children age 6 to
9 years). In addition, the words were spoken similarly in standard Arabic and in both
Jordanian and Saudi dialect, with the speaker remaining as the only potential factor
affecting performance. Another consideration affecting data reported in the present study
is the small sample size. Only 10 participants were included in each group, with three
speakers in each dialect. Although the power analysis requirement was met by statistical
criteria (power .95), a larger sample size would probably yield more representative
The results from the present study are not consistent with Alusi's (1974) contention
that his word lists could be used in all Arab countries since the word lists were derived
from standard Arabic. There are several clear differences between the present study and
the study reported by Alusi. For example, the subjects in Alusi's study were educated
adults, whereas subjects in the present study were children. Also, the sample size (N=
17) in Alusi's study was inadequate for comparison of performance from different
dialectal backgrounds. Furthermore, the speaker in Alusi's study used Baghdad general
dialect, whereas in the present study the speaker used general Jordanian and Saudi
dialects. These differences among studies highlight the need for further investigation to
compare word recognition performance with the speech materials from the present study
with children from other Arabic speaking countries, particularly in relation to
psychometric functions and the familiarity of words.
Further clinical investigations should be conducted utilizing the word lists
developed in this study. A first step would be to collect data for standardization of the
word lists. Several variables must be considered in the collection of standardization data.
Data should be collected from a large sample of the target population that is
representative of rural as well as urban Jordan. In addition, the sample should include
participants of all socioeconomic groups, as well as children who display normal and
impaired hearing abilities. Another step is to establish test reliability. Factors to be
considered are lists versus half list reliability, children who display normal and impaired
hearing, as well as gender (males and females).
Although the present study focused on word recognition, there is a need to develop
other speech diagnostic materials that for evaluation of speech perception abilities of
younger and older pediatric populations, as well as populations with different auditory
problems, such as auditory processing disorder. Diagnostic speech audiometry materials
that are needed clinically include:
* A "word recognition by picture identification test" for children younger than 6
years. The words used in the present study could be assessed for familiarity with
younger children and on the basis of the availability of picture representations of
the words. A picture-pointing word recognition measure would be very helpful in
diagnosing and monitoring benefit of hearing aids and intervention in younger
* Material for central auditory processing diagnosis, such as filtered words, figure
ground tests, and dichotic tests could be developed based on the present word lists.
The fact that the words are digitally recorded facilitates the process of developing
* Speech reception tests for estimation of threshold are lacking for Arabic-speaking
children and adults. The procedures used to develop the speech materials in the
present study have shown to be effective as in other studies by Harris et al. (2003b)
and Nissen et al. (2005b), and can be duplicated.
Results from the present study will play a significant role in the development of
additional Arabic speech audiometry materials and in improving audiological services
provided in Jordan. The technique for recording the speech materials and the data
collection conditions in the present study render these word lists to be of high quality.
The speaker was carefully chosen to have clear speech and representative of general
Jordanian dialect. The words were selected based on familiarity to children who are
native speakers of Arabic in Jordan. The data were collected using headphones, the most
common method for presenting speech signals in this age group. And, finally, the words
were digitally recorded under excellent recording sound conditions providing a very clear
signal, and materials that can be easily duplicated for the production of multiple audio
copies of the word lists and half lists The word recognition materials developed in the
present study are likely to be of considerable value in providing diagnostic services and
intervention to the pediatric hard of hearing population in Jordan. In addition, these word
recognition materials will contribute to the rapid development of diagnostic speech
audiometry materials that are currently lacking in Arabic countries.
Table 5-1 Descriptive statistics for Hard of Hearing and Normal Hearing based on dialect
Hard of Hearing Normal Hearing
Jordanian Saudi Jordanian Saudi
Mean 0.69 0.72 0.74 0.81
Standard Deviation 0.18 0.18 0.13 0.10
Range 0.66 0.76 0.55 0.46
Min 0.30 0.24 0.42 0.52
Max 0.97 1.00 0.97 0.97
The initial pool of potential participants with normal hearing included 18
children; five of them were excluded after the initial pure tone audiometric testing due to
their elevated thresholds that have exceeded the inclusion criteria of 15dB HL at least at
one frequency. Further testing using bone conduction audiometry revealed mild
conductive hearing loss. The children's caregivers were counseled and retest was
recommended. In one case mild sensorineural hearing loss was diagnosed. This
participant was included in the hard of hearing group. Data from 2 participants was
excluded due to technical problems during data collection, which rendered results
unreliable. Among the 20 potential participants in the hard of hearing group seven
participants were excluded for having a severe hearing loss, based on the screening, and
some had developmental delays that they were not able to understand the task.
SPEECH MATERIAL SELECTION
The words in this study were chosen from three different sources: a word
frequency lists provided by Landau (1959) which was derived from daily newspapers
from Lebanon, Egypt, Iraq, and Palestine, and modem prose; lists of Arabic key words
for learners of everyday Arabic as a second language (Quitregard, 1994); and from
children's story books.
To achieve Egen's (1948) criteria for word selection, the researcher selected
words that fit one of the following possible monosyllabic word structures in Arabic: CV,
CVC, CVCC, CVVC, and CVV. According to Altoma (1969) these monosyllabic word
structures are shared by colloquial and standard Arabic. As mentioned above, words were
selected from a range of written material. To ensure that selected words are representative
of spoken Arabic, the procedure employed by Alusi (1974) and Ashoor and Prochazka
(1982 and 1985) were followed. Words that have similar forms in colloquial and standard
Arabic were selected. This step can be supported by Altoma's (1969) finding that 83.5%
of the words in a colloquial form have shared origins with words in standard Arabic.
Words from Landau (1959) and Quitregard (1994) and a list of (135 words) that are
familiar to first, second and third grade Jordanian Arabic speaking students (Abdulhaq,
unpublished) were used. Abdulhaq's familiar word list is composed of monosyllabic
words chosen from children's story books and Jordanian daily newspaper articles, and
rated by 320 first, second, and third grade students as familiar or unfamiliar. The words
were read aloud to the children, they were asked to raise their hand if they knew the
meaning of the word and were able to put it in a meaningful sentence. To ensure the
accuracy of their response, Abdulhaq started with three trial words that were familiar to
children (window, door, and table), randomly asked children to put words in a sentence,
in addition unfamiliar words (from old classical text) and nonsense words were included.
The initial list included 210 words. One hundred and thirty five words were rated as
familiar at least by 80% of the children. Additional words from Landau and Quitregard
then were added. The researcher used three different procedures to ensure the familiarity
of words chosen for the word lists. Monosyllabic words were matched between the word
lists from Landau (1959) and Quitregard (1994). Words that occurred in both lists were
considered for further familiarity testing. Second the matching words and Abdulhaq's list
were rated as familiar or unfamiliar by four Jordanian teachers of first, second and third
grade. Finally teachers' ratings were compared to the children's ratings for consistency.
Out of these lists, the top 304 words rated as highly familiar by students and teachers
were selected for recording.
LOGISTIC REGRESSION ANALYSIS
Logistic regression analysis calculates the probability of a response based on the
independent variable, in this study, the stimulus intensity level. The logistic regression
analysis provides derived variables that are key parameters for the regression line that
may be tested and compared between conditions (in this study, the word lists). The
results from the logistic regression analysis, a, the logistic regression intercept and b, the
logistic regression slope, were used to calculate the probability of correct identification of
the words at each intensity level. Based on the model described by Nissen et al. (2005a)
in the description of performance on the word list in terms of logistic regression, the
following modified equation was used to calculate the percent correct:
Equation (1P=(1- exp(a) x100
1 + exp(a + b x i)
Where P is the percent correct at an intensity level, a is the regression intercept, b is the
regression slope, and i is the intensity level in dB HL. Using the regression intercept,
slope and intensity levels in equation 1 made it possible to predict the percent correct at
any given intensity level.
LIST OF WORDS FOR DIALECT COMPARISON
HALF WORD LISTS AND MEANING
1A 2A 3A 4A
Good Gj God yA Meadow -- Sun
-4 Elbow c Pilgrim J# Horses c Crown
i4 Home Jj Button > Fairies j Figs
UI-j Head Wrap J.L Situation j Well
j4 Market j-1 Fire Y) Answer .-j Brick
-j3 Almonds Tight J.-. Length L Under
JL& Uncle (maternal) j.t Gas 4 Bank a Opposite
LcA Cup Bird -j Whale ^ Poisonous
ay Cold Sleeve ej Wind ? Pocket
AI Family j Free o-a Chick jS Cottage
-uA Bus D Dam Eye Seeds
Hunt i-.j Fear -4 Dry uS Dog
c4 Death Stick Roof Skewer
j3 Husband j Prosperity CjJ Spirit Count
,, Blacks Poured (adj) .ij Shelf ;uji Tub
j West Glamour u2 Person Jug
-3 Dress Tea Tower jij Symbol
-j Valley Shaikh Wilderness Difficult
j3 Geese a China c1 Girl Distance
C Snow j. House u! weather cUJ Lesson
J Elephant -j Turn u J Earth Bitter
SSomething j Hair Jj9 Roses -- Guest
0j Zaid (name) 4 Heart _i1 Nose Thousand
.L Beach c J Feather J Right c. Saturday
cj Voice Religion Love Ducks
oU- Lettuce u]ja Requirement
S Serious e Garlic
iS Palm Sell
, I Touch Armey
Same t- Year
Mother o5 Outfit
cj-J Heads Pilgrimage
L. Trumpet Traffic
S Board Secret
ije Width Serious
. Thorns Day
J; Hot Rice
j Berry y. Space
Check j-. Hunger
-.uj Oil -.J) Rural
S Rooster JU Money
Jj) Turn Youngman
S Party Brother
1. Boot Reem (name)
uL- friendliness Private
L" Neighborhood L- Class
S Monkey LuL Nest
S Treasure L-i- Wool
Gray hair Tide
u Egg > Grandfather
jj Bull String
jjy Banana 3A Shadow
S Bed JL Solution
j,- War j Throne
- Turn over Sword
S Increase Jjs Lima bean
Evil j? Neighbor
L Thief Axe
S Father t Gauze
j> Spicy .) Way
ui- Dive jS Belly
Line j.' Fence
9j Light Third
u9~. Arch jl Mouse
3'-! Lamp 5 Blood
L~- People 1i Sister
La Bear Limit
L4 Core Sleep
-j& Plum j When
S Worry jl Color
& East aj Gun
4; Shower o4 Bag
Uncle (paternal) 4 Skin
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Nadia Abdulhaq was born and raised in Nablus, Palestine. The third of six
children, she graduated from high school in 1990 and enrolled at Birzeit University in
Birzeit, Palestine. She obtained her Bachelor of Arts degree in English language and
literature with a minor in translation, in June, 1995. She then went on to receive her
Master of Arts degree in speech-language pathology from the University of Jordan in
Amman, Jordan, in June, 1997.
After graduating from the University of Jordan, she accepted the position of
speech pathologist in the Department of Rehabilitation at the Palestine Red Crescent
Society. Her job description included establishing three speech therapy units in three
different cities across the West Bank of Palestine and training six rehabilitation workers
as speech therapy assistants. Her work focused on children with hearing loss. After two
years of work and achievements, she decided to return to the academic world and pursue
further knowledge to be able to better serve the hard of hearing and deaf community of
In 2000 she received the Fulbright scholarship to start her doctoral studies at the
University of Florida in Gainesville, Florida. During her five year study she was an active
member of the international student body initiating projects such as the International
Student Speakers Bureau and the international student welcome information table. She
received numerous scholarships and recognition, O. Ruth McQown Scholarship, Grinter
Fellowship, Gibbson Dissertation Fellowship, and the Outstanding Academic
Achievement award. She will graduate in December 2005 with a Ph.D. in communication
sciences and disorders and Doctor of Audiology degrees. Upon her graduation, she will
return to Palestine to start a speech and hearing program for the hard of hearing, new
born hearing screening program, and an audiology clinic.