Time-frequency masking for speech separation and its potential for hearing aid design

A new approach to the separation of speech from speech-in-noise mixtures is the use of time-frequency (T-F) masking. Originated in the field of computational auditory scene analysis, T-F masking performs separation in the time-frequency domain. This article introduces the T-F masking concept and reviews T-F masking algorithms that separate target speech from either monaural or binaural mixtures, as well as microphone-array recordings. The review emphasizes techniques that are promising for hearing aid design. This article also surveys recent studies that evaluate the perceptual effects of T-F masking techniques, particularly their effectiveness in improving human speech recognition in noise. An assessment is made of the potential benefits of T-F masking methods for the hearing impaired in light of the processing constraints of hearing aids. Finally, several issues pertinent to T-F masking are discussed.

Noisy speech recognition using de-noised multiresolution analysis acoustic features

This paper describes a novel application of multiresolution analysis (MRA) in extracting acoustic features that possess de-noising capability for robust speech recognition. The MRA algorithm is used to construct a mel-scaled wavelet packet filter-bank, from which subband powers are computed as the feature parameters for speech recognition. Wiener filtering is applied to a few selected subbands at some intermediate stages of decomposition. For high-frequency bands, Wiener filters are designed based on a reduced fraction of the estimated noise power, making the consonant features much more prominent and contrastive. The proposed method is evaluated in phone recognition experiments with the TIMIT database. In the presence of stationary white noise at 10-dB SNR, the de-noised MRA features attain a phone recognition rate of 32%. There is a noticeable improvement compared with the accuracy of 29% and 20% attained by the commonly used mel-frequency cepstral coefficients (MFCC) with and without cepstral mean normalization (CMN), respectively. The effectiveness of the MRA features is also verified by the fact that they exhibit smaller distortion from clean speech.