INCREASED NOISE IMMUNITY IN LARGE VOCABULARY SPEECH RECOGNITION WITH THE AID OF SPECTRAL SUBTRACTION.

Abstract

Several ways of making the signal processing in the IBM speech recognition system more robust with respect to variations in the background noise level are presented. The underlying problem is that the speech recognition system trains on the specific noise circumstances of the training session. A simple solution is the controlled addition of noise. The level of noise that has to be added in to effectively mask all background noise is rather high and causes a significant reduction in accuracy. Spectral subtraction does a better job in a limited number of cases, but the thresholding in spectral subtraction often leads to training problems in the hidden-Markov-model-based recognition system. The best results were obtained by reintroducing a seminatural background by adding noise after applying spectral subtraction.