Perceptualand Motor Skills, 1990, 70, 931-934. O Perceprual and Motor Skills 1990
USING T H E BINOMIAL MODEL TO EVALUATE SPEECH-DISCRIMINATION SCORES OBTAINED DURING BACKGROUND NOISE ' JOSEPH C. SEVER, JR Old Dominion UniuersiQ Summary.-This study was designed to investigate the variability in performance frequently observed when speech discrimination is assessed in the presence of background noise. Using the binomial distribution, analysis suggests variability is substantial, so speech discrimination scores obtained in noise should be interpreted with caution.
The measurement of speech discrimination during background noise has been widely researched and continues to be an integral component of many audiological protocols (Miller, 1947; Klein, 1989; Papso & Blood, 1989; Tyler & Kuk, 1989). In the clinical environment, the addition of background noise is considered by many clinicians and researchers to enhance the sensitivity of the test and to present a more realistic evaluation of everyday listening situations which frequently occur in the presence of ambient noise (Danhauer, Doyle, & Lucks, 1985). In spite of the popularity of this protocol, a problem that has been mentioned frequently (Gengel, Miller, & Rosenthal, 1981; Loven & Hawkins, 1983) concerns the variabhty of speech-discrimination scores obtained during background noise. Specifically, for grouped data, standard deviations are frequently large. Furthermore, in situations where multiple scores are obtained from the same individual, it has been difficult to assess whether a difference in test-retest scores can be considered a change in performance or one based on chance. In addition, there is no standardization with respect to the actual test protocol for evaluating speech discrimination during background noise. The present study was designed to investigate further the variability of speech discrimination scores obtained during background noise using a probability model based on the binomial distribution. The application of this model to the evaluation of speech discrimination scores was first proposed by Thornton and Raffin (1978, 1980). Subsequent research (Raffin & Schafer, 1980) appeared to support the generality of this model in evaluating scores obtained in a variety of test situations, including background noise.
Requests for reprints should be sent to Joseph C. Sever, Ph.D., Pro ram in Speech Pathology ind Audiology, Child Study Center, Old Dominion University, ~ o r f o f VA , 135294136.
J. C. SEVER, JR.
METHOD Subjects In all, 48 normal-hearing college students participated in the study. For 24 subjects the right ear served as the test ear and the remaining 24 subjects were tested with the left ear. The criterion for selection of subjects specified that pure-tone thresholds were 10 dB Hearing Level or better at octave intervals ranging from 250 to 4000 Hertz.
Stimuli Speech discrimination was assessed using a recorded version (male speaker) of Northwestern Auditory Test No. 6 presented with three types of background noise. Each test list contains 50 monosyllabic words and the subject is required to repeat the word that is presented. For a given test condition, background noise was either white noise, speech spectrum noise, or multitalker babble. The white noise had an amplitude spectrum characterized by equal amplitude across the frequency range. The speech spectrum noise and multitalker babble had similar amplitude spectra that were fairly flat up to 1000 Hertz and then decreased by approximately 6 dB per octave. Signal-to-noise (SIN) ratios of 0, 4, and 8 dB were used for all three noise conditions. To achieve the desired S/N ratio, the level of the speech stimuli was held constant at a level that was 40 dB above each subject's speech-reception threshold and the level of noise was adjusted accordingly. Testing was conducted in a sound-attenuated test room (Industrial Acoustics Company). The tape-recorded stimuli were played on a tape recorder (Ampex) routed through a clinical audiometer (Grason-Stadler) which also served to generate the white and speech spectrum noises.
Procedure Sixteen subjects were randomly assigned to each of the three noise conditions. Each subject was tested at SIN ratios of 0, 4, and 8 dB in a test-retest paradigm. This procedure yielded a total of six scores per subject (two scores for each SIN ratio). The test lists and order of the SIN ratio presentations were randomized for all subjects for each of the three noise conditions. Each subject's responses were verbal and were monitored by the experimenter who scored them as either correct or incorrect. The procedures were identical for all three noise conditions, and each subject participated in only one noise condition.
Data Analysis The data were analyzed using a probabihty model based on the binomial distribution (Thornton & Raffin, 1978; Raffin & Thornton, 1980). This model provides a statistical basis for explaining and evaluating the inherent variability of speech-discrimination scores. According to the model, speech-
BINOMIAL MODEL FOR SPEECH DISCRIMINATION
933
discrimination scores can be described by the binomial distribution which, as Thornton and Raffin (1978) note, is specified completely when we have an estimate of the subject's true score and know the number of words in the speech-discrimination test. When this model was applied to speech-discrimination scores, it was demonstrated that variability (expressed by the standard deviation in percent) was a function of the obtained scores and the number of words in the test (Thornton & Raffin, 1978). Based on thls information, Thornton and Raffin (1978) constructed a table to use in comparing subjects' test-retest scores to estimate whether they are significantly different ( p < .05). To use the table, one finds the first (test) score and then determines whether the second (retest) score is within the corresponding limits of critical differences. If not, the test-retest scores are considered significantly different (p < .05). To evaluate the variability associated with group data (e.g., scores obtained in a particular noise condition), the proportion of subjects' scores falling outside the critical differences is computed (Raffin & Schafer, 1980; Loven & Hawkins, 1983). According to the model, this proportion should be .05. If the presence of background noise increases the variability of the test scores, the computed proportions should be larger than the .05 value predicted by the model. RESULTSAND DISCUSSION To fac~litatecomparison of the present data with previous research (Raffin & Schafer, 1980; Loven & Hawkins, 1983), the total number of comparisons falling outside the critical differences were pooled across all conditions of the study. Table 1 presents this summary. The results of the analysis using the binomial model clearly indicate that speech-discrimination testing in the presence of background noise demonstrated variability that exceeded the model's prediction. The over-all proportion of scores falling outside critical differences (.188) is substantially larger than the value (.05) predicted by the model. TABLE 1 SUMMARY OF SCORESFALLINGOUTSIDECRITICAL Number of Comparisons
Number of Scores Outside Critical Differences
DIFFERENCES Proportion
In general, present results support the fact speech-discrimination scores obtained with background noise show substantial variability in a test-retest situation. The application of the binomial model to study the variability provides additional evidence to suggest that the presence of noise contributes additional sources of variability. Presently, the nature of these sources is unknown. A similar conclusion was reached by Loven and Hawkins (1983),
934
J. C. SEVER, JR
who investigated the interlist equivalency of the CID W-22 speech-discrimination test presented during noise. Analyzing their data with both conventional statistics and the binomial model, they found that 11% (proportion of .11) of their subjects had scores outside the critical differences. Given the present results and information from previous research, caution is indicated in interpreting speech-discrimination scores obtained during background noise. Unambiguous interpretations will not be possible until the sources of increased variability of speech discrimination in noise are known. REFERENCES D A N ~ L AJ.~ L., R , DOYLE, P. C., & LUCKS,L. (1985) Effects of noise on NST and NU 6 stimuli. Ear and Hearing, 6, 266-269. GENGEL, R. W., -R, L., & ROSENTHAL, E. (1981) Between and within listener variability in response to CID W-22 presented in noise. Ear and Hearing, 2, 78-81. KLEIN, A. J. (1989) Assessing speech recognition in noise for listeners with a signal processor hearing aid. Ear and Hearing, 10, 50-57. LOVEN,F, C., & HAWKINS,D. B. (1983) Interlist equivalency of the CID W-22 word lists presented in quiet and noise. Earing and Hearing, 4, 91-97. MILLER, G . A. (1947) The masking of speech. Psychological Bulletin, 44, 105-129. PAPSO,C. S., & BLOOD,I. M. (1989) Word recognition skills of children and adults in background noise. Ear ond Hearing, 10, 235-236. RAFFIN, M. J. M., & SCHAFER,D. (1980) Application of a probability model based on the binomial distribution to speech discrimination scores. Journal of Speech and Hearing Remrch, 23, 570-575. h m ,M. J. M., & THORNTON, A. R. (1980) Confidence levels for differences between speech-discrimination scores: a research note. Journal of Speech and Hearing Research, 23, 5-18. THORNTON, A. R., & RAFFM, M. J. M. (1978) Speech-discrimination scores modeled as a binomial variable. Journal of Speech and Hearing Research, 2 1, 507-5 18. TYLER, R. S., & KUK, F. K. (1989) The effects of "noise suppression" hearing aids on consonant recognition in speech-babble and low-frequency noise. Ear and Hearing, 10, 243-249.
Accepted April 17, 1990.
USING T H E BINOMIAL MODEL TO EVALUATE SPEECH-DISCRIMINATION SCORES OBTAINED DURING BACKGROUND NOISE ' JOSEPH C. SEVER, JR Old Dominion UniuersiQ Summary.-This study was designed to investigate the variability in performance frequently observed when speech discrimination is assessed in the presence of background noise. Using the binomial distribution, analysis suggests variability is substantial, so speech discrimination scores obtained in noise should be interpreted with caution.
The measurement of speech discrimination during background noise has been widely researched and continues to be an integral component of many audiological protocols (Miller, 1947; Klein, 1989; Papso & Blood, 1989; Tyler & Kuk, 1989). In the clinical environment, the addition of background noise is considered by many clinicians and researchers to enhance the sensitivity of the test and to present a more realistic evaluation of everyday listening situations which frequently occur in the presence of ambient noise (Danhauer, Doyle, & Lucks, 1985). In spite of the popularity of this protocol, a problem that has been mentioned frequently (Gengel, Miller, & Rosenthal, 1981; Loven & Hawkins, 1983) concerns the variabhty of speech-discrimination scores obtained during background noise. Specifically, for grouped data, standard deviations are frequently large. Furthermore, in situations where multiple scores are obtained from the same individual, it has been difficult to assess whether a difference in test-retest scores can be considered a change in performance or one based on chance. In addition, there is no standardization with respect to the actual test protocol for evaluating speech discrimination during background noise. The present study was designed to investigate further the variability of speech discrimination scores obtained during background noise using a probability model based on the binomial distribution. The application of this model to the evaluation of speech discrimination scores was first proposed by Thornton and Raffin (1978, 1980). Subsequent research (Raffin & Schafer, 1980) appeared to support the generality of this model in evaluating scores obtained in a variety of test situations, including background noise.
Requests for reprints should be sent to Joseph C. Sever, Ph.D., Pro ram in Speech Pathology ind Audiology, Child Study Center, Old Dominion University, ~ o r f o f VA , 135294136.
J. C. SEVER, JR.
METHOD Subjects In all, 48 normal-hearing college students participated in the study. For 24 subjects the right ear served as the test ear and the remaining 24 subjects were tested with the left ear. The criterion for selection of subjects specified that pure-tone thresholds were 10 dB Hearing Level or better at octave intervals ranging from 250 to 4000 Hertz.
Stimuli Speech discrimination was assessed using a recorded version (male speaker) of Northwestern Auditory Test No. 6 presented with three types of background noise. Each test list contains 50 monosyllabic words and the subject is required to repeat the word that is presented. For a given test condition, background noise was either white noise, speech spectrum noise, or multitalker babble. The white noise had an amplitude spectrum characterized by equal amplitude across the frequency range. The speech spectrum noise and multitalker babble had similar amplitude spectra that were fairly flat up to 1000 Hertz and then decreased by approximately 6 dB per octave. Signal-to-noise (SIN) ratios of 0, 4, and 8 dB were used for all three noise conditions. To achieve the desired S/N ratio, the level of the speech stimuli was held constant at a level that was 40 dB above each subject's speech-reception threshold and the level of noise was adjusted accordingly. Testing was conducted in a sound-attenuated test room (Industrial Acoustics Company). The tape-recorded stimuli were played on a tape recorder (Ampex) routed through a clinical audiometer (Grason-Stadler) which also served to generate the white and speech spectrum noises.
Procedure Sixteen subjects were randomly assigned to each of the three noise conditions. Each subject was tested at SIN ratios of 0, 4, and 8 dB in a test-retest paradigm. This procedure yielded a total of six scores per subject (two scores for each SIN ratio). The test lists and order of the SIN ratio presentations were randomized for all subjects for each of the three noise conditions. Each subject's responses were verbal and were monitored by the experimenter who scored them as either correct or incorrect. The procedures were identical for all three noise conditions, and each subject participated in only one noise condition.
Data Analysis The data were analyzed using a probabihty model based on the binomial distribution (Thornton & Raffin, 1978; Raffin & Thornton, 1980). This model provides a statistical basis for explaining and evaluating the inherent variability of speech-discrimination scores. According to the model, speech-
BINOMIAL MODEL FOR SPEECH DISCRIMINATION
933
discrimination scores can be described by the binomial distribution which, as Thornton and Raffin (1978) note, is specified completely when we have an estimate of the subject's true score and know the number of words in the speech-discrimination test. When this model was applied to speech-discrimination scores, it was demonstrated that variability (expressed by the standard deviation in percent) was a function of the obtained scores and the number of words in the test (Thornton & Raffin, 1978). Based on thls information, Thornton and Raffin (1978) constructed a table to use in comparing subjects' test-retest scores to estimate whether they are significantly different ( p < .05). To use the table, one finds the first (test) score and then determines whether the second (retest) score is within the corresponding limits of critical differences. If not, the test-retest scores are considered significantly different (p < .05). To evaluate the variability associated with group data (e.g., scores obtained in a particular noise condition), the proportion of subjects' scores falling outside the critical differences is computed (Raffin & Schafer, 1980; Loven & Hawkins, 1983). According to the model, this proportion should be .05. If the presence of background noise increases the variability of the test scores, the computed proportions should be larger than the .05 value predicted by the model. RESULTSAND DISCUSSION To fac~litatecomparison of the present data with previous research (Raffin & Schafer, 1980; Loven & Hawkins, 1983), the total number of comparisons falling outside the critical differences were pooled across all conditions of the study. Table 1 presents this summary. The results of the analysis using the binomial model clearly indicate that speech-discrimination testing in the presence of background noise demonstrated variability that exceeded the model's prediction. The over-all proportion of scores falling outside critical differences (.188) is substantially larger than the value (.05) predicted by the model. TABLE 1 SUMMARY OF SCORESFALLINGOUTSIDECRITICAL Number of Comparisons
Number of Scores Outside Critical Differences
DIFFERENCES Proportion
In general, present results support the fact speech-discrimination scores obtained with background noise show substantial variability in a test-retest situation. The application of the binomial model to study the variability provides additional evidence to suggest that the presence of noise contributes additional sources of variability. Presently, the nature of these sources is unknown. A similar conclusion was reached by Loven and Hawkins (1983),
934
J. C. SEVER, JR
who investigated the interlist equivalency of the CID W-22 speech-discrimination test presented during noise. Analyzing their data with both conventional statistics and the binomial model, they found that 11% (proportion of .11) of their subjects had scores outside the critical differences. Given the present results and information from previous research, caution is indicated in interpreting speech-discrimination scores obtained during background noise. Unambiguous interpretations will not be possible until the sources of increased variability of speech discrimination in noise are known. REFERENCES D A N ~ L AJ.~ L., R , DOYLE, P. C., & LUCKS,L. (1985) Effects of noise on NST and NU 6 stimuli. Ear and Hearing, 6, 266-269. GENGEL, R. W., -R, L., & ROSENTHAL, E. (1981) Between and within listener variability in response to CID W-22 presented in noise. Ear and Hearing, 2, 78-81. KLEIN, A. J. (1989) Assessing speech recognition in noise for listeners with a signal processor hearing aid. Ear and Hearing, 10, 50-57. LOVEN,F, C., & HAWKINS,D. B. (1983) Interlist equivalency of the CID W-22 word lists presented in quiet and noise. Earing and Hearing, 4, 91-97. MILLER, G . A. (1947) The masking of speech. Psychological Bulletin, 44, 105-129. PAPSO,C. S., & BLOOD,I. M. (1989) Word recognition skills of children and adults in background noise. Ear ond Hearing, 10, 235-236. RAFFIN, M. J. M., & SCHAFER,D. (1980) Application of a probability model based on the binomial distribution to speech discrimination scores. Journal of Speech and Hearing Remrch, 23, 570-575. h m ,M. J. M., & THORNTON, A. R. (1980) Confidence levels for differences between speech-discrimination scores: a research note. Journal of Speech and Hearing Research, 23, 5-18. THORNTON, A. R., & RAFFM, M. J. M. (1978) Speech-discrimination scores modeled as a binomial variable. Journal of Speech and Hearing Research, 2 1, 507-5 18. TYLER, R. S., & KUK, F. K. (1989) The effects of "noise suppression" hearing aids on consonant recognition in speech-babble and low-frequency noise. Ear and Hearing, 10, 243-249.
Accepted April 17, 1990.
