A model distance measure for talker clustering and identification

This paper describes methods of talker clustering and identification based on a "distance" metric between discrete HMM output probabilities. Output probabilities are derived on a tree-based MMI partition of the feature space, rather than the usual vector quantization. The information divergence (relative entropy) between speaker-dependent models is used as a quantitative measure of how much a given talker differs from another talker. An immediate application is talker identification: an unknown speaker may be identified by finding the closest speaker-dependent reference model to a model trained on the unknown speaker's data. Another application is to cluster similar talkers into a group; these may be used to train a HMM model that represents that talker better than a more general model. It is shown that using the model "nearest" a novel talker enhances the performance of a talker-independent speech recognition system.<<ETX>>