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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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: Although Dawson et al. [4] found that fitting a psychometric function provided the best SRT test-retest reliability, there are some limitations in this approach. Occasionally, a subject’s average scores are not a monotonically increasing function of SNR; an example is shown in Fig 1. This could be due to random fluctuation, or a lapse in the subject’s concentration, or a run of more difficult sentences (despite efforts to equalise sentence difficulty [4]). Such cases can produce a poor fit. Furthermore, the fitting method assumed a binomial distribution [5], but the assumption that a sentence containing K words consists of K independent Bernoulli trials is violated, because recognition of one word is not independent of the other words. Sentences representative of everyday conversation have contextual cues, meaning that if the subject recognises the first few words, then they are more likely to recognise the remaining words. At the SRT, although the average word score is 50%, it is relatively uncommon to score near 50% for any particular sentence; instead some sentences receive scores near 100%, and a roughly equal number of sentences receive scores near 0%. A histogram of the sentence scores for Study One (described in the next section), with large spikes at values 0% and 100%, is shown in Fig 2. |
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Affiliation: Department of Statistics, Macquarie University, Sydney, NSW, Australia.
No MeSH data available.