Modelling of the interframe dependence in an HMM using conditional Gaussian mixtures

✍ Scribed by Ji Ming; F.Jack Smith

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 332 KB
• Volume: 10
• Category: Article
• ISSN: 0885-2308
• DOI: 10.1006/csla.1996.0012

✦ Synopsis

This paper investigates the modelling of the interframe dependence in a hidden Markov model (HMM) for speech recognition. First, a new observation model, assuming dependence on multiple previous frames, is proposed. This model represents such a dependence structure with a weighted mixture of a set of first-order conditional Gaussian densities, each mixture component accounting for a specific conditional frame. Next, an optimization in choosing the conditional frames/segment is performed in both training and recognition, thereby helping to remove the mismatch of the conditional segments due to different observation histories. An EM (Expectation-Maximization) iteration algorithm is developed for the estimation of the model parameters and for the optimization over the dependence structure. Experimental comparisons on a speaker-independent E-set database show that the new model, without optimization on the dependence structure, achieves better performance than the standard HMM, the bigram HMM and the linear-predictive HMM, all in comparable or smaller parameter sizes. The optimization over the dependence structure leads to further improvement in the performance.