SPEECH DEREVERBERATION WITH A REVERBERATION TIME SHORTENING TARGET

This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning tar- get for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for net- work training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of re- verberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverber- ation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance.