Noise Model Transfer: Novel Approach to Robustness Against Nonstationary Noise

Abstract

This paper proposes an approach, called noise model transfer (NMT), for estimating the rapidly changing parameter values of a feature-domain noise model, which can be used to enhance feature vectors corrupted by highly nonstationary noise. Unlike conventional methods, the proposed approach can exploit both observed feature vectors, representing spectral envelopes, and other signal properties that are usually discarded during feature extraction but that are useful for separating nonstationary noise from speech. Specifically, we assume the availability of a noise power spectrum estimator that can capture rapid changes in noise characteristics by leveraging such signal properties. NMT determines the optimal transformation from the estimated noise power spectra into the feature-domain noise model parameter values in the sense of maximum likelihood. NMT is successfully applied to meeting speech recognition, where the main noise sources are competing talkers; and reverberant speech recognition, where the late reverberation is regarded as highly nonstationary additive noise.