Survey of Feature Normalization Techniques for Robust Speech Recognition
Weiping Ye · Zhongwen xinxi xuebao · 2010
The performance of current automatic speech recognition(ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources.Such performance degradation is mainly caused by mismatch between the training and recognition environments.Quite a few techniques have been proposed to reduce this mismatch over the past several years.Some of the techniques,like feature-based normalization,are generally simple yet powerful to provide robustness against several forms of signal degradation.So normalization strategies are often chosen as the preferred method for speech robustness.They are employed by normalizing the statistical properties(moment),cumulative density function or power spectral density(PSD) of feature vector to compensate for the effects of environmental mismatch.In this paper,most commonly used feature normalization methods are reviewed,such as cepstral moment normalization,histogram equalization technique(HEQ) and Modulation Spectrum Normalization etc.