An SII-based approach to predict the speech intelligibility in fluctuating noise for normal-hearing listeners

Abstract

The speech intelligibility index (SII) is frequently used to predict the speech intelligibility for speech in a given interfering noise. However, the SII model only has been validated for speech in stationary noise. Since the SII departs from speech and noise spectra, it does not take into account any fluctuations in the masking noise. Hence, the model will yield similar SII values, regardless of the degree of fluctuation. In contrast, from the literature it is clear that normal-hearing listeners can benefit from the fluctuations in the noise. The present paper describes an SII-based approach to model speech reception thresholds (SRTs) for speech in both stationary and fluctuating noise. The basic principle of this approach is that both speech and noise signals are partitioned into small time frames. Within each time frame, the conventional SII is determined, yielding the speech information available to the listener at that time frame. Next, the SII values of these time frames are averaged, resulting in the SII for that particular condition. With the aid of SRT data from the literature, it will be shown that this approach can give a good account for most existing data.