Comparison of text-independent speaker recognition methods using VQ-distortion and discrete/continuous HMM´s

This paper compares a VQ (vector quantization)-distortion-based speaker recognition method and discrete/continuous ergodic HMM (hidden Markov model)-based ones, especially from the viewpoint of robustness against utterance variations. The authors show that a continuous ergodic HMM is as robust as a VQ-distortion method when enough data is available and that a continuous ergodic HMM is far superior to a discrete ergodic HMM. They also show that the information on transitions between different states is ineffective for text-independent speaker recognition. Therefore, the speaker recognition rates using a continuous ergodic HMM are strongly correlated with the total number of mixtures irrespective of the number of states