Speaker change detection and speaker clustering using VQ distortion for broadcast news speech recognition

Addresses the problem of the detection of speaker changes and clustering speakers when no information is available regarding speaker classes or even the total number of classes. We assume that no previous information on speakers is available (no speaker model, no training phase) and that people do not speak simultaneously. The aim is to apply speaker grouping information to speaker adaptation for speech recognition. We use vector quantization (VQ) distortion as the criterion. A speaker model is created from successive utterances as a codebook by a VQ algorithm, and the VQ distortion is calculated between the model and an utterance. A result was obtained by the experiment on speaker detection and speaker clustering. The speaker change detection experiment was compared with results by generalized likelihood ratio and Bayesian information criterion. We show the superiority of our proposed method.