A new binary mask based on noise constraints for improved speech intelligibility

It has been shown that large gains in speech intelligibility can be obtained by using the binary mask approach which retains the time-frequency (T-F) units of the mixture signal that are stronger than the interfering noise (masker) (i.e., SNR>0 dB), and removes the T-F units where the interfering noise dominates. In this paper, we introduce a new binary mask for improving speech intelligibility based on noise distortion constraints. A binary mask is designed to retain noise overestimated T-F units while discarding noise underestimated T-F units. Listening tests were conducted to evaluate the new binary mask in terms of intelligibility. Results from the listening tests indicated that large gains in intelligibility can be achieved by the application of the proposed binary mask to noise-corrupted speech even at extremely low SNR levels (-10 dB).