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.
Highlights
Measuring speech intelligibility in noise is an important endeavor in the clinical management of hearing loss
We propose an alternative approach, a zero-and-one inflated beta regression model, in which an observation is a single sentence score rather than an speech reception threshold (SRT)
Each adaptive track used a list of 20 sentences, and the SRT was calculated as the mean of the signal-to-noise ratio (SNR) of the final 16 sentences
Summary
Measuring speech intelligibility in noise is an important endeavor in the clinical management of hearing loss. It can be used to assess the benefit a person receives from a hearing aid or cochlear implant, and to track their performance over time. It is used in the research and PLOS ONE | DOI:10.1371/journal.pone.0132409. Processing algorithms that may be incorporated into future Cochlear Ltd. products This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials. A common approach is to play a pre-recorded sentence, mixed with noise, to the subject, who attempts to verbally repeat it. The clinician records a score for the sentence based on the number of words that the subject repeated correctly. A subject is typically tested with a list of 10 to 32 sentences taken from a corpus of sentences compiled for this purpose [1] [2] [3] [4]
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