A comparative study of discrete, semicontinuous, and continuous hidden Markov models

Abstract

In this paper, we first extended the semicontinuous hidden Markov model to incorporate multiple code-books. The robustness of the semicontinuous output probability is enhanced by the combination of multiple codewords and multiple codebooks. In addition, we compared the semicontinuous model with the continuous mixture model and the discrete model in a large-vocabulary speaker-independent continuous speech recognition (DARPA resource management) task. The model assumption and parameter size issues are addressed in particular through these experiments. When the acoustic parameters are not well modelled by the continuous probability density, the model assumption problems may cause the recognition accuracy of the semicontinuous model or the continuous mixture model to be inferior to the discrete model. We also found that the SCHMM can have a large number of free parameters in comparison with the discrete HMM because of its smoothing ability. With explicit male and female clustered models and for conditional feature sets, we were able to reduce the error rate of discrete-model-based SPHINX by more than 20%.