An algorithm for the dynamic inference of hidden Markov models (DIHMM)

Authors:
Philip Lockwood;Marc Blanchet
Affiliations:
Matra Communication, Corporate Scientific and Technical Division, Bois D'Arcy Cedex, France;Matra Communication, Corporate Scientific and Technical Division, Bois D'Arcy Cedex, France
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

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Abstract

The DIHMM algorithm performs a robust estimation of the HMM topology and parameters. It allows a better control of the speech variability within each state of the HMM, yielding enhanced estimates. The DIHMM parameters (number of states, structure of the Gaussian mixture density functions (modes), transition matrix) are obtained from the training data via probabilistic grammatical inference techniques, welded in a Viterbi-like training framework. Experimental results on various databases indicate a global improvement of the recognition rates in adverse environments: the results averaged on three databases show an increase of 12.8% on raw data and 2.4% when using NSS [11].