Download AUTHORS MR. MAINAK SANTRA AUDIOLOGIST AND SPEECH

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
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