A model for predicting speech intelligibility
Hannes Müsch, So ren Buus · The Journal of the Acoustical Society of America · 2001
A statistical-decision theory based model for predicting speech intelligibility is introduced. This model, which we call the speech-recognition sensitivity (SRS) model, aims at predicting speech-recognition performance from the long-term average speech spectrum, the masking excitation in the listener’s ear, the linguistic entropy of the speech material, and the number of response alternatives available to the listener. Unlike articulation-index (AI) models, the SRS model can account for synergetic and redundant interactions among spectral bands of speech. It also accounts for effects of linguistic entropy and number of response alternatives on intelligibility scores without resorting to the empirically determined ad hoctransformations employed by AI models. The effects of linguistic entropy are modeled by an entropy-dependent central noise, which modifies the listener’s identification sensitivity to the speech. The effect of the number of response alternatives on the test score is a direct consequence of using statistical decision theory. The SRS model also appears to predict how the effect of linguistic entropy varies with the filter condition and how linguistic entropy and language proficiency interact with the signal-to-noise ratio. Fits of the SRS model to data from the literature and consonant-discrimination data collected in our laboratory will be presented. [Work supported by NIH and SigmaXi.] a)Current affiliation: GN ReSound Corporation.