Sixth European Conference on Speech Communication and Technology
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.
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Bibliographic reference. Kolano, Guido / Regel-Brietzmann, Peter (1999): "Combination of vector quantization and gaussian mixture models for speaker verification with sparse training data", In EUROSPEECH'99, 1203-1206.