Speech-in-noise enhancement using amplification and dynamic range compression controlled by the speech intelligibility index.

Abstract

In many speech communication applications, such as public address systems, speech is degraded by additive noise, leading to reduced speech intelligibility. In this paper a pre-processing algorithm is proposed that is capable of increasing speech intelligibility under an equal-power constraint. The proposed AdaptDRC algorithm comprises two time- and frequency-dependent stages, i.e., an amplification stage and a dynamic range compression stage that are both dependent on the Speech Intelligibility Index (SII). Experiments using two objective measures, namely, the extended SII and the short-time objective intelligibility measure (STOI), and a formal listening test were conducted to compare the AdaptDRC algorithm with a modified version of a recently proposed algorithm in three different noise conditions (stationary car noise and speech-shaped noise and non-stationary cafeteria noise). While the objective measures indicate a similar performance for both algorithms, results from the formal listening test indicate that for the two stationary noises both algorithms lead to statistically significant improvements in speech intelligibility and for the non-stationary cafeteria noise only the proposed AdaptDRC algorithm leads to statistically significant improvements. A comparison of both objective measures and results from the listening test shows high correlations, although, in general, the performance of both algorithms is overestimated.