Speaker Recognition with Small Training Requirements Using a Combination

Vector Quantisation (VQ) has been shown to be robust in speaker recognition systems which require a small amount of training data. However the conventional VQ-based method only uses distortion measurements and discards the sequence of quantised codewords. In this paper we propose a method which extends the VQ distortion method by combining it with the likelihood of the sequence of VQ indices against a discrete hidden Markov model (DHMM). The method is particularly suitable for combined speech recognition and speaker recognition systems. Experiments on the TI46 database show that the combined score gives better performance than both the conventional VQ-based and DHMM-based methods.