A Statistical Method for the Analysis of Speech Intelligibility Tests.
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A set of SRT results is typically analyzed with a repeated measures analysis of variance.Confidence intervals for the fitted value curves are obtained by parametric bootstrap.Another advantage of the new method of analysis is that results are stated in terms of differences in percent correct scores, which is more interpretable than results from the traditional analysis.
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PubMed Central - PubMed
Affiliation: Department of Statistics, Macquarie University, Sydney, NSW, Australia.
ABSTRACT
Speech intelligibility tests are conducted on hearing-impaired people for the purpose of evaluating the performance of a hearing device under varying listening conditions and device settings or algorithms. The speech reception threshold (SRT) is typically defined as the signal-to-noise ratio (SNR) at which a subject scores 50% correct on a speech intelligibility test. An SRT is conventionally measured with an adaptive procedure, in which the SNR of successive sentences is adjusted based on the subject's scores on previous sentences. The SRT can be estimated as the mean of a subset of the SNR levels, or by fitting a psychometric function. A set of SRT results is typically analyzed with a repeated measures analysis of variance. We propose an alternative approach for analysis, a zero-and-one inflated beta regression model, in which an observation is a single sentence score rather than an SRT. A parametrization of the model is defined that allows efficient maximum likelihood estimation of the parameters. Fitted values from this model, when plotted against SNR, are analogous to a mean psychometric function in the traditional approach. Confidence intervals for the fitted value curves are obtained by parametric bootstrap. The proposed approach was applied retrospectively to data from two studies that assessed the speech perception of cochlear implant recipients using different sound processing algorithms under different listening conditions. The proposed approach yielded mean SRTs for each condition that were consistent with the traditional approach, but were more informative. It provided the mean psychometric curve of each condition, revealing differences in slope, i.e. differential performance at different parts of the SNR spectrum. Another advantage of the new method of analysis is that results are stated in terms of differences in percent correct scores, which is more interpretable than results from the traditional analysis. No MeSH data available. Related in: MedlinePlus |
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Mentions: Speech in noise tests are sometimes performed at a fixed signal-to-noise ratio (SNR), to give a percent-correct measure of intelligibility. If the test is repeated at different SNRs, and the scores are plotted as a function of SNR, the resulting curve is known as a psychometric function that is typically S-shaped, for example the logistic function [5]. When designing a study into the effects of a sound processing algorithm, the differences in performance between subjects can be so large that testing all subjects at the same SNR would be prone to floor or ceiling effects. An alternative is to measure the speech reception threshold (SRT) of each subject, which is typically defined as the SNR at which the subject scores 50% correct. The SRT is conventionally estimated by an adaptive procedure, in which the SNR of each sentence is adjusted based on the subjectâ€™s previous responses. Adaptive threshold estimation methods were initially developed for experiments in which the subject provides a binomial response on each trial [6]; for example, identifying the correct interval in an N-alternative forced-choice test. These methods can readily be applied to sentence tests; if the subject correctly identifies more than half of the words in the sentence, then the SNR is reduced (making the next sentence more difficult), and conversely if the subject correctly identifies less than half of the words, then the SNR is increased (making the next sentence easier). With an appropriate adaptive rule, the SNR should converge to the SRT [3]. Two example adaptive tracks are shown in Fig 1. |
View Article: PubMed Central - PubMed
Affiliation: Department of Statistics, Macquarie University, Sydney, NSW, Australia.
No MeSH data available.