State Divergence-Based Determination of The Number of Gaussian Components of Each State in HMM

A new, state divergence-based algorithm is proposed in this paper to determine the number of Gaussian components of each state in continuous density HMM by maximizing the between-state divergence. The unscented transform based approximation of the Kullback-Leibler divergence is adopted to measure the between-state model divergence to direct the determination. Due to the advantage of being more discriminative, the proposed approach can lead to more compact HMM. Our experimental evaluation shows that compared with the conventional Bayesian Information Criterion based determination (which is better than the uniform determination), the presented method can reduce the total number of Gaussian components to about 63%, while it results in almost negligible degradation of the recognition performance.