Abstract
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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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