Improving a GMM speaker verification system by phonetic weighting

Abstract

This paper compares two approaches to speaker verification, Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The GMM based system outperformed the HMM system, this was mainly due to the ability of the GMM to make better use of the training data. The best scoring GMM frames were strongly correlated with particular phonemes, e.g. vowels and nasals. Two techniques were used to try and exploit the different amounts of discrimination provided by the phonemes to improve the performance of the GMM based system. Applying linear weighting to the phonemes showed that less than half of the phonemes were contributing to the overall system performance. Using an MLP to weight the phonemes provided a significant improvement in performance for male speakers but no improvement has yet been achieved for women.