Abstract

The performance of automatic speech recognition (ASR) systems degrades greatly when speech is corrupted by noise. Missing feature methods attempt to reduce this degradation by deleting components of a time-frequency representation of speech (such as a spectrogram) that exhibit low signal-to-noise ratio (SNR). Recognition is then performed using only the remaining components of the incomplete spectrogram. These methods have been shown to result in recognition accuracies that are very robust to the effects of additive noise. However, conventional missing feature methods, which modify the classifier used to perform the recognition, suffer from the drawback that they are constrained to use the log-spectral vectors of the spectrogram as features for recognition. It is well known recognition systems that use log-spectral features perform poorly compared to systems that use cepstral features. In this

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