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AUTHORS MR. MAINAK SANTRA AUDIOLOGIST AND SPEECH THERAPIST HEARING PLUS, Opposite Gajkumar Brothers,, 4, Moti Sil St, Dharmatala, Taltala, Kolkata, West Bengal 700013 Ph no- 9831413770, Mail id- [email protected] Corresponding Author MR.INDRANIL CHATTERJEE LECTURER ALI YAVAR JUNG NATIONAL INSTITUTE FOR THE HEARING HANDICAPPED EASTERN REGIONAL CENTER B.T. ROAD, NIOH CAMPUS BONHOOGHLY, KOLKATA-700090. Ph no-9433102816 Mail id- [email protected] MS. ARPITA CHATTERJEE SHAHI AUDIOLOGIST AND SPEECH THERAPIST ALI YAVAR JUNG NATIONAL INSTITUTE FOR THE HEARING HANDICAPPED EASTERN REGIONAL CENTER B.T. ROAD, NIOH CAMPUS BONHOOGHLY, KOLKATA-700090. Ph no- 8820688172, Mail id- [email protected] PSYCHOMETRIC VALIDATION OF PHONEMICALLY BALANCED WORD LISTS IN BENGALI MAINAK SANTRA,1 INDRANIL CHATTERJEE,2 ARPITA CHATTERJEE SHAHI3 ABSTRACT Speech audiometry is an essential component in the battery of audiological tests battery. The plethora of research studies advocates that during speech audiometry an individual needs to be tested in his or her own native language. The present study aimed to develop word lists that exhibited familiarity, homogeneity and phonemic balance. Twenty five items were finalized from the parent list. Standardization of the test material was done on thirty native normal hearing subjects. Validity measures were obtained by comparing the CID W-22 lists to thirty native speakers with L1 Bengali and L2 English. The slopes of the mean psychometric functions for wordlist one and two were 3.5%/dB and 3.6%/ dB respectively. The homogeneity of the word lists was examined by evaluating the inter-list equivalence, inter-word variability and inter-subject variability. The inter-word and inter-subject variability for both the list was 4.3dB and 4.5 dB; and 3.5 dB and 3.4 dB respectively. Binomial probability confirmed that the two lists were essentially equivalent at all presentation level. Based on the above findings it can be concluded that the material can be used to assess word recognition scores for the native Bengali speakers. Keywords: phonemic balance, psychometric functions, reliability, validity. For the successful rehabilitation for the persons with hearing impairment, the first step is to conduct a comprehensive audiological assessment. As an integral part of assessment speech audiometry had been used traditionally to determine the speech threshold and speech recognition. The dialectical difference between two languages makes it difficult to use it in the nonnative speakers. Even, if two languages have similar sound system they may vary significantly in their physical characteristics "including both acoustic and articulatory characteristics.21 Word recognition testing is further dramatically influenced by the phonetic, melodic, and intonational differences among languages.6 An individual being tested in a non-native language would not be familiar with the majority of the presented words. This could result in the stimulus words becoming nonsense test items.16 The individuals who have learned English as a second language after puberty perform significantly worse on speech audiometry tests than those who are native speakers of English.13 Even individuals, who are considered bilingual, have been shown to perform poorly on speech audiometry tests as compared to monolingual speakers of the test language. 8, 11, 13, 20, 22 So to serve the colloquial population the authors developed a test material in Bengali. This language occupies the second position among all the scheduled 22 languages of India. It is spoken by 83, 369,769 people in India (8.11% of the total population of India).7 Normally, we associate the term phonetic balance (or PB for short) with word recognition scores. But the term phonetic balance had been used erroneously. It has been realized that the test material used to assess word recognition scores should have a phonemic composition equivalent to that of everyday speech “Phonemically balanced” words that is, the different phonemes in the test material should occur with the same relative frequencies as in everyday speech. The rationale is; the lucidity of a listener is more competent with a more frequent phoneme compared to a less frequent one in his daily life. METHODS The aim of the present study was the psychometric validation of Phonemically Balanced word lists in Bengali, for the purpose of assessing word recognition scores. PARTICIPANTS The study included four groups of participants. The first group of participants included five native speakers with L1 Bengali. They independently rated the lists of monosyllabic words for familiarity. The second group consisted of thirty native speakers with L1 Bengali and L2 English (13 male, 17 female), they participated for the evaluation of monosyllabic words. Further these subjects were screened for no positive history of ear diseases and normal hearing. The third group was another 30 normal hearing subjects who apart from being native speakers of Bengali were also proficient in speaking English. The final group had three subjects with hearing impairment; the first of them was diagnosed as Bilateral Moderately Severe Sensori-Neural hearing loss, the second diagnosed as having Moderately Severe Mixed hearing loss in right ear and Profound hearing loss in left ear and the third subject was diagnosed as Bilateral Moderate Conductive hearing loss. Consent letter from the participants were taken for ethical consideration. INSTRUMENTATION The 30 subjects were tested in a double room audiological testing suite. MAICO MA 53 Dual Channel Diagnostic Audiometer calibrated using ANSI (2004) specifications.1 The stimulus (both pure tones and speech) were delivered monoaurally through the Telephonics TDH – 39 supra – aural headphones. PROCEDURE PHASE – I: Development of the test material The test material consisted of commonly used monosyllabic words in Bengali, with no duplicate words across the two lists. The CVC syllable structure was used. The lists consisted of words with same relative frequencies as in everyday speech. The average difficulty and the range of difficulty of each list was equivalent to that of the other list. Monosyllabic words from two sources.9,18 Familiarity was tested by 5 adult native speakers of Bengali. Judgment was done on a 3 point rating scale. The familiarity ratings was given weight ages such as – most familiar (+3), fairly familiar (+2), very unfamiliar (+1). For the purpose of consonant balancing the number of times a consonant should appear was calculated using the following e.g.: the phoneme /r/ appears 8.24 times out of 100 phonemes spoken or written, so when the number of phonemes is 150, the occurrence of /r/ is: Number of occurrence for a phoneme (in %) × 150 100 So, each word list should have twelve (12) /r/ phonemes approximately. After having calculated the occurrence of each phoneme out of 150, fraction or decimal value was rounded off using to the next higher number. Finally, fifty PB wordlist were alphabetically developed. PHASE – II: Standardization of the test material For the psychometric or performance – intensity functions for the word lists. The 30 normal hearing subjects evaluated the monosyllabic words by listening and responding to the words. They were provided with two to three rest periods of about 5 minutes in between. The subjects were presented with the word list at various intensity levels ranging from 10 to 70 dBHL. Each subject listened and responded to approximately 1200 presentations (600 words for each ear). The order of the words and the presentation levels were randomized. They were not made familiar with the word list prior to the commencement of the test. The word lists were presented using monitored live voice (MLV) through the stereo live speech microphone. Speech microphone was kept constant at six inches. A thin paper separator was used between the nose and the mouth of the tester/speaker to prevent the unnecessary noise generated due to nasal emissions. Each word was preceded by the carrier phrase “|apni bolun|” (say the word). PHASE – III: Correlation of live vs. recorded voice sample Adobe audition CS6 and Nuendo 6 softwares were used to record and edit the live voice and converted to recorded voice for analysis. The recorded speech was presented via Sennheiser- HD 202 II headphones with microphone. Scoring The tester marked the subject’s response as either correct or incorrect on a response/score sheet. A sample of the response sheet is provided at Appendix – I. Test retest and validity was measured. Half List formation In order to develop the half list, the threshold (or the 50% recognition point) in dBHL for each word in both the wordlist 1 and 2 was calculated. The words were then arranged in descending order from most difficult to the easiest. The original wordlist 1 and 2 was thus divided to form two half lists. STATISTICAL METHOD Mean and standard deviation at which the decade interval percent correct recognitions were obtained for the wordlist one and two was calculated for the subject and for the words. It was hypothesized that There will be no significant difference between the word recognition scores obtained between the CID W-22 and the developed PB word list (H0: μ against the alternative hypothesis Ha: μ Wordlist 1 & 2 Wordlist 1 & 2 = μ CID W - 22 ≠ μ CID W - 22). Two tailed tests were used to test the hypotheses. The null hypothesis in all the three cases was rejected if the value of the test statistics (S) was greater than 1.96 at 5 % level of significance. RESULTS AND DISCUSSION This study was done to develop and standardize the materials to be used to assess word recognition scores of the native speakers of Bengali. Test materials in other languages used with the native speakers of Bengali, will not give an exact estimation of the subject’s speech recognition ability. Hence, there was a need to develop and standardize test materials in Bengali to assess the speech recognition ability of an individual speaking Bengali. Development of the test material Collection of the familiar monosyllabic words and familiarity rating – The commonly used monosyllabic words was collected from two sources. The first source18 had a pool of words arranged in the descending order of frequency. From this pool of words, only the monosyllabic words with a CVC structure were taken. The count of words collected from this pool was only 119. This pool of 119 words was not sufficient to form two phonemically balanced word lists, hence a second source was chosen. The second source9 provided a huge database of words also arranged in descending order of frequency. From this second source 377 monosyllabic words with a CVC syllable structure were drawn. Thus, we had a pool of 496 CVC words. The words that appeared common to both the lists and having English origin (but spoken in Bengali) were eliminated. This reduced the pool of CVC monosyllabic words to 299 words. Among all the 299 words, 255 words were rated as most familiar, forty three as fairly familiar. The word /hɔn/ was rated 'unfamiliar' by all the raters and was therefore eliminated from the list. From the final pool of 298 words two separate lists were made taking into consideration the frequency of distribution of phonemes in Bengali language.18 Each list consisted of 50 monosyllabic CVC words. The following Table 1 and 2 shows sample of the final distribution of phonemes of the two lists. The two phonemically balanced word lists are provided in Appendix – II. Table 1 Distribution of phonemes for Wordlist 1 Phonemes Frequency of occurrence of the phonemes in Bengali Language Number of times the phonemes should appear in List 1 (calculated value) Number of times the phonemes appear in List 1 /k/ 4.74% ~ 5 7.5 ~ 8 8 /kʰ/ 1.04% ~ 1 1.5 ~ 2 2 /ɡ/ 1.27% ~ 1 1.5 ~ 2 2 /ɡʰ/ 0.18% ~ 1 1.5 ~ 2 1 /ɔ/ 4.99% ~ 5 7.5 ~ 8 9 /a/ 9.86% ~ 10 15 23 Consonants Vowels Table 2 Distribution of phonemes for Wordlist 2 Phonemes Frequency of Number of times Number of times occurrence of the the the phonemes appear in List 1 in phonemes phonemes actually in List 2 Bengali Language (calculated value) /k/ 4.74% ~ 5 7.5 ~ 8 8 /kʰ/ 1.04% ~ 1 1.5 ~ 2 2 /ɡ/ 1.27% ~ 1 1.5 ~ 2 2 /ɡʰ/ 0.18% ~ 1 1.5 ~ 2 2 /ɔ/ 4.99% ~ 5 7.5 ~ 8 6 /a/ 9.86% ~ 10 15 19 /e/ 9.28% ~ 9 13.5 ~ 14 4 Consonants Vowels In Bengali, the phonemes /s/ and /ʃ/ are allophonic variations of the same phoneme /ʃ/. Hence, while preparing the phonemically balanced word lists they were not included separately. Similar inclusionary criteria were applied for the phoneme /r/ also; which again has two allophonic variants flap /ɾ/ and trill /r/. The allophonic variant of /h/ i.e. /ɦ/ was not considered in the list because /ɦ/ always occurs in combination with other sound. It occurs initially as the first member in combination with /r/ and /l/ and medially with /r/.4 The consonants in each lists closely approximates to the frequency of occurrence of the consonants.18 The present study showed good agreement between the speech recognition and pure tone thresholds. Further live voice correlated with recorded voice at α 0.82 at p=0.05. Psychometric Functions for the wordlist 1 and 2 The data (both threshold and supra-threshold) that was obtained in speech audiometry is represented as points on a psychometric function. The graphic plot that relates some aspect of patient performance (output) to a stimulus dimension (input). Typically, patient performance is plotted on the ordinate (y – axis) and the level of the signal expressed in dBHL is plotted on the abscissa (x – axis). The word recognition psychometric function is described by two characteristics; the first is the location of the function in the Cartesian co – ordinate system and second by the slope of the function. 27,28 Table 3 and Table 4 illustrate the mean [percent correct recognition (and standard deviations) for Wordlist 1 and Wordlist 2 from 30 listeners with normal hearing. The mean psychometric functions for the Wordlist 1 and 2 obtained from the 30 normal hearing subjects are shown in Figure 1 and Figure 2 respectively. Table 3 The mean percent correct recognition (and standard deviations) for Wordlist 1 from 30 listeners with normal hearing N Mean Std. Deviation 10 dBHL 13 11.0000 5.46260 20 dBHL 30 45.8000 16.07483 30 dBHL 30 79.5333 13.01559 40 dBHL 30 96.2667 4.69355 50 dBHL 30 99.6667 1.05230 60 dBHL 30 100.0000 .00000 70 dBHL 30 100.0000 .00000 Table 4 The mean percent correct recognition (and standard deviations) for Wordlist 2 from 30 listeners with normal hearing N Mean Std. Deviation 10 dBHL 13 10.3462 4.65568 20 dBHL 30 46.7667 17.69072 30 dBHL 30 81.4333 12.04563 40 dBHL 30 97.0000 3.63598 50 dBHL 30 99.8333 .55744 60 dBHL 30 100.0000 .00000 70 dBHL 30 100.0000 .00000 One essential finding from Table 3 and 4 was that for both the lists as the presentation level increased the mean percent correct recognition also increased. However the variability decreases as the presentation level increases. The variability in percentage of correct recognition was more at 20 and 30 dBHL for both the lists. These values when plotted on a Cartesian co – ordinate system represents the mean psychometric function of the test material. WORD LIST 1 120 96.2 Percent Correct Recognition 100 99.6 100 100 80 79.5 60 45.8 WORD LIST 1 40 20 11 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Figure 1. The mean psychometric functions for Wordlist 1 from 30 normal hearing subjects. The mean 100% recognition point for Wordlist 1 was obtained around 50 dBHL. The curve tends to saturate beyond 50 dBHL. The slope of the mean functions were calculated using the traditional linear slope that assumes a linear relation between the 20 percent and 80 percent correct points and was calculated as Δy/Δx. The slope of the mean functions for the Wordlist 1 was calculated using the above mentioned procedure manually and the value was 3.5 percent/dB. Word List 2 120 97 99.3 100 100 Percent Correct Recognition 100 81.4 80 60 46.7 Word List 2 40 20 10.34 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Figure 2. The mean psychometric functions for Wordlist 2 from 30 normal hearing subjects. The mean 100% recognition point for Wordlist 2 is obtained around 50 dB HL. The slope of the mean functions for the Wordlist 2 was calculated manually and the value was 3.6 percent/dB. Figure 3 is a comparison of the mean psychometric functions between Wordlist 1 and Word List 2 for the 30 normal hearing subjects. Figure 4 and Figure 5 represents the mean psychometric functions for each of the 30 normal hearing subjects for Wordlist 1and 2 respectively. Figure 6 and Figure 7 shows the mean psychometric functions for each of the words for the 30 subjects. Comparison of the Mean data of Wordlist 1 and Wordlist 2 110 96.2 99.6 100 100 99.8 100 100 Percent Correct Recognition 100 90 79.5 80 97 81.4 70 60 45.8 50 46.7 40 Word List 1 Word List 2 30 20 11 10.3 10 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Figure 3 The mean psychometric functions for the Wordlist 1 and 2 from 30 individuals with normal hearing. From Figure 3 it is evident that there is no significant difference between the list scores at all presentation level. Three essential findings from Figure 1 – 3 is that: The psychometric function tends to increase linearly at low presentation level for both the lists. The psychometric function tends to saturate beyond 50 dBHL for both the list and the slope of the mean functions for both the lists was essentially steeper and similar but between the 20% and 80% point the mean psychometric function of wordlist 2 is slightly higher than the wordlist 1. This configuration explicitly derives the pattern of less complexity in list 2. The slopes of the mean functions for our wordlist 1 and 2 came out as 3.5 percent/dB and 3.6 percent per dB respectively which is similar to the values previously reported for word recognition materials developed in English.2 The performance norms 15 for the VA compact disc versions of CID W-22 and PB-50 word lists have substantially different slope characteristics of 3.1 percent/dB and 1.7 percent/dB. Psychometrically equivalent Russian speech audiometry materials 14 by male and female talkers and found that the psychometric slope functions for monosyllabic words were 5.8%/dB for male talker and 5.6%/dB for female talker. There is a direct relation between variability of the test items and the slope of the mean psychometric function.14 The more homogeneous performance is on the individual test items with respect to both location and slope, the steeper the slope of the mean psychometric function. The following section addresses this issue with respect to the individual participants and the individual test items. 120 Psychometric Functions for the subjects along with the mean data (Wordlist 1) 100 Percent Correct Recognition 96.2 80 99.6 100 100 Sub 1 Sub 2 Sub 3 Sub 4 Sub 5 Sub 6 Sub 7 Sub 8 Sub 9 Sub 10 Sub 11 Sub 12 Sub 13 Sub 14 Sub 15 Sub 16 Sub 17 Sub 18 Sub 19 Sub 20 Sub 21 79.5 60 45.8 40 20 11 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Figure 4 The mean psychometric functions for each of the 30 normal hearing subjects for Wordlist 1. Psychometric Functions for the subjects along with the Mean Data 120 100 99.8 100 100 Percent Correct Recognition 97 80 81.4 60 46.7 40 20 10.3 Sub 1 Sub 2 Sub 3 Sub 4 Sub 5 Sub 6 Sub 7 Sub 8 Sub 9 Sub 10 Sub 11 Sub 12 Sub 13 Sub 14 Sub 15 Sub 16 Sub 17 Sub 18 Sub 19 Sub 20 Sub 21 Sub 22 Sub 23 Sub 24 Sub 25 Sub 26 Sub 27 Sub 28 Sub 29 Sub 30 Mean 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Figure 5 The mean psychometric functions for each of the 30 normal hearing subjects for Wordlist 2. Mean Psychometric Functions for the individual words in Wordlist 1 120 100 Percent Correct Recognition 80 60 40 20 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Word 1 Word 2 Word 3 Word 4 Word 5 Word 6 Word 7 Word 8 Word 9 Word 10 Word 11 Word 12 Word 13 Word 14 Word 15 Word 16 Word 17 Word 18 Word 19 Word 20 Word 21 Word 22 Word 23 Word 24 Word 25 Word 26 Word 27 Word 28 Word 29 Word 30 Word 31 Word 32 Word 33 Word 34 Word 35 Word 36 Word 37 Word 38 Word 39 Word 40 Word 41 Word 42 Word 43 Word 44 Word 45 Word 46 Word 47 Word 48 Word 49 Word 50 Figure 6 Shows the mean psychometric functions for each of the words for the 30 subjects. Mean Psychometric Functions for the individual words in Wordlist 2 120 100 Percent Correct Recognition 80 60 40 20 0 10 dBHL 20 dBHL 30 dBHL 40 dBHL 50 dBHL 60 dBHL 70 dBHL Presentation Level (dBHL) Word 1 Word 2 Word 3 Word 4 Word 5 Word 6 Word 7 Word 8 Word 9 Word 10 Word 11 Word 12 Word 13 Word 14 Word 15 Word 16 Word 17 Word 18 Word 19 Word 20 Word 21 Word 22 Word 23 Word 24 Word 25 Word 26 Word 27 Word 28 Word 29 Word 30 Word 31 Word 32 Word 33 Word 34 Word 35 Word 36 Word 37 Word 38 Word 39 Word 40 Word 41 Word 42 Word 43 Word 44 Word 45 Word 46 Word 47 Word 48 Figure 7 Shows the mean psychometric functions for each of the words for the 30 subjects. Table 5 Mean and the standard deviation (dBHL) at which the decade interval percent correct recognitions were obtained for the Wordlist 1. The means of the standard deviations are shown in parenthesis (for subjects) and [for words] % Correct Wordlist 1 Mean dBHL S.D. For subjects 10 for Mean dBHL for S.D. subjects words words 11.0 0.8 11.6 20 12.7 1.8 14.5 2.9 30 16.7 3.2 16.9 3.5 40 20.0 3.2 18.7 4.3 50 22.4 3.6 21.1 4.5 60 24.5 4.0 23.8 4.7 70 27.0 3.7 26.8 4.5 80 30.1 3.8 29.9 4.5 90 35.3 4.0 34.8 5.4 100 50.3 6.1 46.4 7.2 S.D. the for S.D. for subjects and words) (3.5) the 1.4 S.D. for words subjects MEAN (of the for [4.3] Table 6 Shows the mean and the standard deviation (dBHL) at which the decade interval percent correct recognitions were obtained for the Wordlist 2. The means of the standard deviations are shown in parenthesis (for subjects) and [for words] % Correct Wordlist 2 Mean dBHL S.D. for the Mean For subjects subjects for words words 10 10.6 .5 12.6 2.9 20 14.0 3.2 14.5 3.4 30 17.1 3.5 17.0 3.8 40 19.8 3.4 19.1 4.1 50 22.0 3.3 21.2 4.6 60 24.1 3.4 23.4 5.0 70 26.1 3.4 26.2 4.8 80 29.7 3.5 29.3 4.8 90 34.0 3.5 33.5 4.9 100 49.6 6.1 45.0 6.7 S.D. MEAN (of the S.D. for subjects and words) for dBHL S.D. for the S.D. subjects words (3.4) [4.5] for Figure 4-7 represents the intersubject and interword variability graphically for Wordlist 1 and 2. One key finding from Figure 6 and 7 is that at least in both the list there are no such words that had failed to attain 100% recognition at the highest presentation level. However, the threshold of audibility was different for each word, indicating that some words were easier than the others. The mean threshold of audibility for Wordlist 1 and 2 were obtained at 22.4 dBHL and 22 dBHL. The four crucial findings that was obtained from Table 5 and 6 are: Intersubject variability is similar for wordlist 1 and 2 (average S.D.’s 3.5 dB and 3.4 dB for wordlist 1 and wordlist 2 respectively); For both the set of materials intersubject variability is lower than the interword variability (average S.D.’s for wordlist 1, 3.5 dB vs. 4.3 dB and for wordlist 2, 3.4 dB vs. 4.5 dB) The interword variability is similar for both the wordlist 1 and 2 (average S.D.’s 4.3 dB and 4.5 dB for wordlist 1 and wordlist 2 respectively) and; The threshold of audibility for both the lists is similar. A comparison of the CID W – 22 lists and the PAL PB – 5027 shows that the intersubject variability is about the same for the two sets of materials Comparing the slope value, interitem variability and intersubject variability of our test material with that of Mandarin Monosyllable Recognition test 26 (MMRT), revealed that the mean slope of the MMRT is steeper than ours. This owes to small inter-item and inter-subject variability. Hence, the more homogeneous or less variable is the psychometric characteristics of the words, the steeper the slope of the mean psychometric function27 is justified from a comparison of the above mentioned study. Inter-list Equivalency The hypothesis that was formulated regarding the equivalency of the test material was that, there would be no significant difference between Wordlist 1 and Wordlist 2 for a fixed intensity i.e. the two lists would be equivalent. Based on the obtained data from the 30 normal hearing subjects a test for equality of Binomial probability was performed to determine if there is any difference significant difference among the lists. The null hypothesis was tested against the alternative hypothesis computing the test statistic (S) at α = 0.05. The computed value of the test statistics at all the presentation level is shown in Table 7. Table 7 Shows the values of the test statistic (S) calculated at 10, 20, 30, 40, 50, 60 and 70 dBHL Presentation 10 20 30 40 50 60 70 Level dBHL dBHL dBHL dBHL dBHL dBHL dBHL Calculated 0.5 1.25 1.83 1.66 1.42 0 0 value of S Since the calculated values of S at all presentation level were less than 1.96 the null hypothesis was accepted at 5% level of significance. Based on the data of inter-subject variability, inter-word variability and inter-list variability we concluded that the developed Bengali wordlists are essentially homogenous i.e. both the lists are similar in terms of average difficulty and range of difficulty hence, both the lists can be used interchangeably. Determination of Presentation Level The mean SRT of the 30 subjects on whom the test was standardized was 14.7 dBHL. From the table 5 and 6 the 100% recognition point for both the list came was 50.3 dBHL and 49.6 dBHL for list 1 and 2 respectively. This 100% recognition point was approximately 35 dBSL (100% recognition point – mean SRT) above the mean SRT. Hence, it can be concluded that during a single presentation of the test list to assess supra-threshold speech recognition score the presentation level should be taken as 35 dBSL relative to the SRT. Formation of Half Lists Historically, audiologists have utilized 50-item lists to establish word recognition scores. To prevent patient fatigue and reduce the time of clinical testing, some audiologists present only half of the full-list items. 23 Thus, the speech audiometry materials that were developed often contain both full lists and half lists. In the present study, the final Wordlists 1 and 2 that was developed to assess word recognition scores in the native speakers of Bengali was divided to form two half list from each parent list. For the purpose of the present study, the psychometric function for each of the word was plotted. From the graph, the threshold or the 50% point on the psychometric function for each of the word in each list was calculated. For each list the words were then rank ordered. The word requiring the highest intensity level to attain 50% recognition, occupied the first position in the list and the word requiring the lowest intensity level to attain 50% recognition, occupied the last position. Thus, the words were arranged from most difficult to the easiest. During this arrangement of words, it was found that there were several words having similar ranks. The words which were having similar grades or ranks were arranged in alphabetical order. From this rearranged list of words that was obtained for each list the first 25 words was placed in one list and the second 25 words were placed in the second list. Thus, the original Wordlist 1 and 2 was divided to form 2 half – lists for each list. These half – lists were termed as 1A and 1B that were developed from Wordlist 1 and 2A and 2B that was developed from Wordlist 2. The sample of half – lists 1A, 1B and 2A, 2B is shown in Appendix - III. The rank order of the words in Wordlist 1 and 2 arranged from most difficult to easiest are shown in Table 12 and Table 13 respectively. Table 12 Rank order of the words in Wordlist 1 arranged from most difficult to easiest Order of the words of Wordlist 1 (from Mean intensity level (dBHL) required to harder to easier) in IPA attain 50% recognition point bʰɔr 33 bat̪ 31 ɡun 30 pʰɔl 27 bʰut̪ 26 Table 13 Rank order of the words in Wordlist 2 arranged from most difficult to easiest Order of the words of Wordlist 2 (from Mean intensity level (dBHL) required to harder to easier) in IPA attain 50% recognition point Nin 33 ɡuɽ 30 ʃak 29 bʰiɽ 29 ɡʰun 29 nat̪ ʰ 27 In an attempt to reduce the test size of the CID W – 22, and W – 22 lists, researchers suggested a speech recognition testing strategy that involves presenting the most difficult words first.24 They recommend presenting the 10 most difficult words first and stopping if these are all correct. If any of the first 10 words are missed, then the next 15 words would be given, for a total of 25 words. Testing would then be terminated if there are no more than four errors based on the first 25 words. Otherwise, the entire 50 wordlist would be used. A similar testing strategy can also be adopted for our test material also. In the present study in an attempt to form half lists the actual phonemic balance of the list was lost. Similar problem regarding half lists were also encountered by other researchers. 5, 10, 19, 23 Test – Retest Reliability A test for equality of Binomial Probability was performed to find out if there is any significant difference between the tests and retest scores obtained from the same subjects after one week. The null hypothesis was tested against the alternative hypothesis computing the test statistic (S) at α = 0.05. The computed values of the test statistic at all the presentation level for wordlist 1 and 2 are shown in Table 8 and Table 9 respectively. Since the calculated values of S at all presentation level were less than 1.96 the null hypothesis was accepted at 5% level of significance. Table 8 Shows the value of the test statistic (S) calculated at 10, 20, 30, 40, 50, 60 and 70 dBHL for wordlist 1 Presentation 10 20 30 40 50 60 70 Level dBHL dBHL dBHL dBHL dBHL dBHL dBHL Calculated 1.44 0.875 0.616 0.932 1.42 0 0 value of S Table 9 Shows the value of the test statistic (S) calculated at 10, 20, 30, 40, 50, 60 and 70 dBHL for Wordlist 2 Presentation 10 20 30 40 50 60 70 Level dBHL dBHL dBHL dBHL dBHL dBHL dBHL Calculated 1.06 0.823 1.008 1.18 1.75 0 0 value of S Validity The hypothesis that was formulated regarding validation of the test material was that, there would be no significant difference between the word recognition scores obtained with the CID W – 22 and the developed phonemically balanced wordlists in Bengali for the normal hearing subjects. A test for equality of Binomial Probability was performed to find out the same. The null hypothesis was tested against the alternative hypothesis computing the test statistic (S) at α = 0.05. The computed values of the test statistic are shown in Table 10. Table 10 Shows the calculated value of the test statistic (S) Calculated Wordlist 1 vs. Wordlist 1 vs. Wordlist 1 vs. Wordlist 1 vs. CID W – 22 CID W – 22 CID W – 22 CID W – 22 List 1 List 2 List 3 List 4 0.6 0.403 0.403 1.49 Wordlist 2 vs. Wordlist 2 vs. Wordlist 2 vs. Wordlist 2 vs. CID W – 22 CID W – 22 CID W – 22 CID W – 22 List 1 List 2 List 3 List 4 1.40 0.495 0.495 0.490 Values of S Calculated Values of S Since the calculated values of S computed by comparing the scores of Wordlist 1 and 2 with all the four list of W - 22 are less than 1.96, the null hypothesis was accepted at 5% level of significance. From this, we can speculate that statistically there is no significant difference between the scores obtained with the CID W – 22 (wordlists 1, 2, 3 and 4) and the developed test material (wordlists 1 and 2). The test material was also administered to assess word recognition in people with hearing impairment. The word recognition scores were obtained with Wordlist 1 and 2 presented to the subjects at 35 dBSL relative to their speech reception threshold. The scores obtained with the wordlists were poorer at the suprathreshold level. For conductive hearing loss, the recognition was markedly better at suprathreshold level but for the sensorineural and mixed hearing loss, the recognition was poor. Similar findings on suprathreshold speech recognition scores in hearing impaired listeners are documented. 3 SUMMARY AND CONCLUSION Assessment of speech perception is a key to successful management of a person with hearing impairment. The present study was thus designed to develop such test materials in Bengali. The effort had resulted in the development of a speech material in Bengali that can be used to assess speech recognition scores in the native speakers of Bengali. However, there remains a great need for further investigation of the developed test material. Therefore, future research needs to examine list equivalency for individuals of varying ages, as well as listeners with varying types and degrees of hearing impairment. Appendix I Sample Scoring Sheet 1 Words 0 2 3 4 5 6 7 1 0 0 0 0 0 0 d d d d d B B B B B Words 2 3 4 5 6 7 0 0 0 0 0 0 0 d d d d d d d d B B B B B B B B d B কর ক াঁচ কন কক ন কর খুব কখল খল গুণ গুড় গল গন Appendix II Bengali Phonemically Balanced Wordlist 1 and Wordlist 2 (with IPA) in alphabetical order Wordlist - 1 Bengali Wordlist – IPA Bengali IPA 2 1. কর kɔɾ 1. ক াঁচ kãtʃ 2. কন kan 2. কক ন Kon 3. কর kar 3. খুব kʰub 4. কখল kʰel 4. খল kʰal 5. গুণ ɡun 5. গুড় ɡuɽ Appendix III Half – Lists 1A, 1B and 2A, 2B Half – List 1A Half – List 1B bʰɔr ভর kan কান bat̪ বাত ɡʰum ঘুম ɡun গুণ dʒoɾ জ ার pʰɔl ফল ʈan টান ɖim ডিম hal হাল bʰut̪ ভুত baɾ বার dʒʰãk ঝাাঁক dʒɔp প Half – list 2a and 2B Half – List 2A Half – List 2B Nin ডনন bɔɾ বর ɡuɽ গুড় kʰub খুব ɡʰun ঘুণ ɖak িাক bʰiɽ ভীড় kon জকান ʃak শাক kʰal খাল REFERENCES 1. American National Standards Institute. (2004). Specification for audiometers (ANSI S3.6–2004). New York: ANSI. 2. Beattie, R. C., Svihovec, D. V., & Edgerton, B. J. (1978). 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