Using statistical decision theory to predict speech intelligibility. I. Model structure.

Abstract

This article introduces a new model that predicts speech intelligibility based on statistical decision theory. This model, which we call the speech recognition sensitivity (SRS) model, aims to predict 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. A major difference between the SRS model and other models with similar aims, such as the articulation index, is this model's ability to account for synergetic and redundant interactions among spectral bands of speech. In the SRS model, linguistic entropy affects intelligibility by modifying 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 the model structure. The SRS model also appears to predict the differential effect of linguistic entropy on filter condition and the interaction between linguistic entropy, signal-to-noise ratio, and language proficiency.