A Novel Speech Recognition Model Utilizing Duration Correlation Information

Abstract

In order to overcome the defects of the duration modeling of homogeneous HMM in speech recognition and the unrealistic assumption that successive observations are independent and identically distribution within a state, Markov family model (MFM), a new statistical model is proposed in this paper. Independence assumption is placed by conditional independence assumption in Markov family model. We have successfully applied Markov family model to speech recognition and propose duration distribution based MFM recognition model (DDBMFM) which takes duration distribution into account and integrates the frame and segment based acoustic modeling techniques. The speaker independent continuous speech recognition experiments show that this new recognition model have higher performance than standard HMM recognition models.