Comparison of text-independent speaker recognition methods using VQ-distortion and discrete/continuous HMMs

A VQ (vector quantization)-distortion-based speaker recognition method and discrete/continuous ergodic HMM (hidden Markov model)-based ones are compared, especially from the viewpoint of robustness against utterance variations. It is shown that a continuous ergodic HMM is far superior to a discrete ergodic HMM. It is also shown that the information on transitions between different states is ineffective for text-independent speaker recognition. Therefore, the speaker identification rates using a continuous ergodic HMM are strongly correlated with the total number of mixtures irrespective of the number of states. It is also found that, for continuous ergodic HMM-based speaker recognition, the distortion-intersection measure (DIM), which was introduced as a VQ-distortion measure to increase the robustness against utterance variations, is effective