Study on Speech Dereverberation with Autocorrelation Codebook
Tomohiro Nakatani, Biing-Hwang Fred Juang, Takafumi Hikichi, Takuya Yoshioka, Keisuke Kinoshita, Marc Delcroix, Masato Miyoshi · 2007
This paper proposes a new speech dereverberation approach based on a statistical speech model. An autocorrelation codebook is introduced as a model that can represent time-varying short-time speech characteristics corresponding to the cepstrum and harmonics. The speech dereverberation is formulated as a likelihood maximization problem, in which the quality of a speech signal is recovered by turning the signal into one that is probabilistically more like a clean speech. Two dereverberation algorithms are derived based on different scenarios, regularized inversion and inverse filter estimation. Experimental results show that the proposed approach allows us to reduce both reverberation and noise with the regularized inversion, and to estimate inverse filters that can dereverberate signals effectively from just a small number of observed signals.