Cepstrum-domain acoustic feature compensation based on decomposition of speech and noise for ASR in noisy environments

Abstract

This paper presents a set of acoustic feature pre-processing techniques that are applied to improving automatic speech recognition (ASR) performance on noisy speech recognition tasks. The principal contribution of this paper is an approach for cepstrum-domain feature compensation in ASR which is motivated by techniques for decomposing speech and noise that were originally developed for noisy speech enhancement. This approach is applied in combination with other feature compensation algorithms to compensating ASR features obtained from a mel-filterbank cepstrum coefficient front-end. Performance comparisons are made with respect to the application of the minimum mean squared error log spectral amplitude (MMSE-LSA) estimator based speech enhancement algorithm prior to feature analysis. An experimental study is presented where the feature compensation approaches described in the paper are found to greatly reduce ASR word error rate compared to uncompensated features under environmental and channel mismatched conditions.