Thu-1-10-1 A Semi-blind Source Separation Approach for Speech Dereverberation

Ziteng Wang(Alibaba Group), Yueyue Na(Alibaba Group), Zhang Liu(Alibaba Group), Yun Li(Alibaba Group), Biao Tian(Alibaba Group) and Qiang Fu(Alibaba Group)
Abstract: This paper presents a novel semi-blind source separation approach for speech dereverberation. Based on a time independence assumption of the clean speech signals, direct sound and late reverberation are treated as separate sources and are separated using the auxiliary function based independent component analysis (Aux-ICA) algorithm. We show that the dereverberation performance is closely related to the underlying source probability density prior and the proposed approach generalizes to the popular weighted prediction error (WPE) algorithm, if the direct sound follows a Gaussian distribution with time-varying variances. The efficacy of the proposed approach is fully validated by speech quality and speech recognition experiments conducted on the REVERB Challenge dataset.
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