Environment-independent continuous speech recognition using neural networks and hidden Markov models

Abstract

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Environment-independent continuous speech recognition is important for the successful development of speech recognizers in real world applications. Linear compensation methods do not work well if the mismatches between training; and testing environments are not linear. In this paper, a neural network compensation technique is explored to mitigate the distortion resulting from additive noise, distant-talking, or telephone channels. The advantage of the neural network compensation method is that retraining of a speech recognizer for each particular application is avoided. Furthermore, since neural networks are trained to transform distorted speech feature vectors to those corresponding to clean speech, it may outperform a retrained speech recognizer trained on distorted speech. Three experiments are conducted to evaluate the capability of the neural network compensation method; recognition of additive noisy speech, distant-talking speech, and telephone speech.