Combination of vector quantization and gaussian mixture models for speaker verification with sparse training data

Kolano, Guido; Regel-Brietzmann, Peter

doi:10.21437/Eurospeech.1999-308

Combination of vector quantization and gaussian mixture models for speaker verification with sparse training data

Guido Kolano, Peter Regel-Brietzmann

We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the verification phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method are only slightly higher than for pure vector quantization.