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.
Cite as: Kolano, G., Regel-Brietzmann, P. (1999) Combination of vector quantization and gaussian mixture models for speaker verification with sparse training data. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 1203-1206, doi: 10.21437/Eurospeech.1999-281
@inproceedings{kolano99_eurospeech,
author={Guido Kolano and Peter Regel-Brietzmann},
title={{Combination of vector quantization and gaussian mixture models for speaker verification with sparse training data}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={1203--1206},
doi={10.21437/Eurospeech.1999-281}
}