Single-Channel Online Enhancement of Speech Corrupted by Reverberation and Noise
Clement S. J. Doire, Mike Brookes, Patrick A. Naylor, Christopher M. Hicks, Dave Betts, Mohammad A. Dmour, Søren Holdt Jensen · IEEE/ACM Transactions on Audio Speech and Language Processing · 2016
This paper proposes an online single-channel speech enhancement method designed to improve the quality of speech degraded by reverberation and noise. Based on an autoregressive model for the reverberation power and on a hidden Markov model for clean speech production, a Bayesian filtering formulation of the problem is derived and online joint estimation of the acoustic parameters and mean speech, reverberation, and noise powers is obtained in mel-frequency bands. From these estimates, a real-valued spectral gain is derived and spectral enhancement is applied in the short-time Fourier transform (STFT) domain. The method yields state-of-the-art performance and greatly reduces the effects of reverberation and noise while improving speech quality and preserving speech intelligibility in challenging acoustic environments.