Noise Perturbation Improves Supervised Speech Separation

Speech separation can be treated as a mask estimation problem where interference-dominant portions are masked in a timefrequency representation of noisy speech. In supervised speech separation, a classifier is typically trained on a mixture set of speech and noise. Improving the generalization of a classifier is challenging, especially when interfering noise is strong and nonstationary. Expansion of a noise through proper perturbation during training exposes the classifier to more noise variations, and hence may improve separation performance. In this study, we examine the effects of three noise perturbations at low signal-to-noise ratios (SNRs). We evaluate speech separation performance in terms of hit minus false-alarm rate and short-time objective intelligibility (STOI). The experimental results show that frequency perturbation performs the best among the three perturbations. In particular, we find that frequency perturbation reduces the error of misclassifying a noise pattern as a speech pattern.