Abstract
A multi-stream approach to utilizing the inherently large number of spectro-temporal features for speech recognition is investigated in this study. Instead of reducing the featurespace dimension, this method divides the features into streams so that each represents a patch of information in the spectrotemporal response field. When used in combination with MFCCs for speech recognition under both clean and noisy conditions, multi-stream spectro-temporal features provide roughly a 30% relative improvement in word-error rate over using MFCCs alone. The result suggests that the multi-stream approach may be an effective way to handle and utilize spectro-temporal features for speech applications.
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