Clustering Linear Dynamic Models for the use of context-dependent models on Speech Recognition
Linear Dynamic Models (LDM) have already been shown to be able to model speech and give high performance on phone recognition tasks. In this project, an approach of using LDMs with triphones as context-dependent models is presented. These contextdependent phones allow the models to deal with coarticulation. Since the number of possible triphones is very large, clustering is performed to determine which models can share parameters. The algorithm presented uses a binary decision tree where decisions are based on phonetic questions. An alternative approach to the likelihood calculation is presented for driving the clustering, and some distance measures are presented and analysed. For dealing with the noise distributions and the initial state, Bhattacharyya distance and Kullback-Lieber divergence are proposed, and for the matrices some definitions are discussed based on the Singular Value Decomposition and a Polar Decomposition of the matrices. Experiments have been performed in order to evaluate the performance of the proposed system. The task chosen was a phone classification on the TIMIT database. The best system on the TIMIT standard test set and without language model give an accuracy of 59.43%. The baseline with monophones was 56.94% which means and absolute improvemenet of 2.49%. This performanced was achieved by clustering the observation noise of the models using Bhattacharyya distance.