Abstract
The performance levels of most current speech recognizers degrade significantly when environmental noise occurs during use. Such performance degradation is mainly caused by mismatches in training and operating environments. During recent years much effort has been directed to reducing this mismatch. This paper surveys research results in the area of digital techniques for single microphone noisy speech recognition classified in three categories: noise resistant features and similarity measurement, speech enhancement, and speech model compensation for noise. The survey indicates that the essential points in noisy speech recognition consist of incorporating time and frequency correlations, giving more importance to high SNR portions of speech in decision making, exploiting task-specific a priori knowledge both of speech and of noise, using class-dependent processing, and including auditory models in speech processing.
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