Weighted Viterbi algorithm and state duration modelling for speech recognition in noise

Abstract

A weighted Viterbi algorithm (HMM) is proposed and applied in combination with spectral subtraction and cepstral mean normalization to cancel both additive and convolutional noise in speech recognition. The weighted Viterbi approach is compared and used in combination with state duration modelling. The results presented show that a proper weight on the information provided by static parameters can substantially reduce the error rate, and that the weighting procedure improves better the robustness of the Viterbi algorithm than the introduction of temporal constraints with a low computational load. Finally, it is shown that the weighted Viterbi algorithm in combination with temporal constraints leads to a high recognition accuracy at moderate SNRs without the need of an accurate noise model.