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Hidden Markov models estimation via the most informative stopping times for Viterbi algorithm

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1 Author(s)
J. A. Kogan ; Courant Inst. of Math. Sci., New York Univ., NY, USA

We propose a sequential approach for studying the Viterbi algorithm via a renewal sequence of the most informative stopping times which allows us in particular to obtain new asymptotic “single-letter” decoding conditions of equivalency between the Baum-Welch, segmental K-means and vector quantization algorithms of the hidden Markov models parameters estimation which have important applications in speech recognition

Published in:

Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on

Date of Conference:

17-22 Sep 1995