Weighted distance measures for efficient reduction of Gaussian mixture components in HMM-based acoustic model

In this paper, two weighted distance measures; the weighted K-L divergence and the Bayesian criterion-based distance measure are proposed to efficiently reduce the Gaussian mixture components in the HMM-based acoustic model. Conventional distance measures such as the K-L divergence and the Bhattacharyya distance consider only distribution parameters (i.e. mean and variance vectors of Gaussian pdfs). Another example considers only mixture weights. In contrast to them, the two proposed distance measures consider both distribution parameters and mixture weights. Experimental results showed that the component-reduced acoustic models created using the proposed distance measures were more compact and computationally efficient than those created using conventional distance measures.