Abstract
The hidden Markov model (HMM) is a double stochastic process. The observable process produces a sequence of observations and the hidden process is a Markov process. The HMM assumes that the occurrence of one observation is statistically independent of the occurrence of the others. To avoid this limitation, a temporal HMM is proposed. The hidden process in the temporal HMM is the same, but the observable process is now a Markov process. Each observation in the training sequence is assumed to be statistically dependent on its predecessor, and codewords or Gaussian components are used as states in the observable Markov process. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database shows a better performance for the temporal HMM compared to the standard HMM.
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