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ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition

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3 Author(s)
V. Digalakis ; SRI Int., Menlo Park, CA, USA ; J. R. Rohlicek ; M. Ostendorf

A nontraditional approach to the problem of estimating the parameters of a stochastic linear system is presented. The method is based on the expectation-maximization algorithm and can be considered as the continuous analog of the Baum-Welch estimation algorithm for hidden Markov models. The algorithm is used for training the parameters of a dynamical system model that is proposed for better representing the spectral dynamics of speech for recognition. It is assumed that the observed feature vectors of a phone segment are the output of a stochastic linear dynamical system, and it is shown how the evolution of the dynamics as a function of the segment length can be modeled using alternative assumptions. A phoneme classification task using the TIMIT database demonstrates that the approach is the first effective use of an explicit model for statistical dependence between frames of speech

Published in:

IEEE Transactions on Speech and Audio Processing  (Volume:1 ,  Issue: 4 )