Speech recognition using phoneme HMM constrained by frame correlation

Abstract

One of the problems with the hidden Markov model (HMM) in performing speech recognition is that the local transition information of the feature vectors is not incorporated into the mechanism of the model and the model is not constrained by transitions of the feature vectors. Thus, the output probability distribution never changes during recognition. Furthermore, all transitions between the vectors that have high probabilities are allowed even if those transitions did not appear in the training data. This paper proposes a bigram‐constrained HMM that uses correlations between two frames to constrain the feature distributions of a speaker‐independent HMM to the region most appropriate for the speaker. Since the output probability of the bigram‐constrained HMM is a conditional probability restricted by the feature vector of the previous frame, the output probability changes dynamically at each frame depending on the feature vector of the previous frame. Constraining the feature distribution makes it possible to reduce the overlapping of feature distributions between different phonemes which improves recognition performance. Previously, we proposed the discrete bigram‐constrained HMM which is based on the combination of a discrete speaker‐independent HMM and the VQ‐code bigram. We showed that it performed better than conventional speaker‐independent HMMs. In this paper, the strategy is extended to the tied‐mixture bigram‐constrained HMM and the continuous bigram‐constrained HMM to obtain better recognition performance. These three types of HMMs are formulated and evaluated by phoneme recognition in continuous speech.