An algorithm for the dynamic inference of hidden Markov models (DIHMM)
IEEE International Conference on Acoustics Speech and Signal ProcessingPublished 1 January 1993
P. Lockwood, Marie‐Anne Blanchet
Citations10
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TL;DR
The DIHMM algorithm performs a robust estimation of the HMM topology and parameters, yielding enhanced estimates that indicate a global improvement of the recognition rates in adverse environments.
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, 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 (nonlinear spectral subtraction).>
Keywords
Computer Science
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