Feature denoising for speech separation in unknown noisy environments
Yuxuan Wang, DeLiang Wang · 2013
Speech separation has been recently formulated as a classification problem. Classification as a form of supervised learning usually performs well on background noises when parts of them are seen in the training set. However, the performance can be significantly worse when generalizing to completely unseen noises. In this study, we present a method that alleviates the generalization issue by attempting to denoise acoustic features before training and testing. We show that a standard multilayer perceptron with proper regularization performs well on this task. Experimental results indicate that the resulting separation system performs significantly better in a variety of unknown noises in low SNR conditions. In a negative SNR condition, we also show that the proposed system produces more intelligible speech according to two recently proposed objective speech intelligibility measures.