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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. We 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. We also show that the information on transitions between different states isdoi:10.1109/89.294363 fatcat:eaiv3gaslfea5lxz3w6tjjpqjq