Yoshihoko Goto

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Continuous density observation hidden Markov models (CD-HMMs) have been shown to perform better than their discrete counterparts. However, because the observation distribution is usually represented with a mixture of multi-variate normal densities, the training time for a CD-HMM can be prohibitively long. This paper presents a new approach to speed-up the(More)
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