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

Digalakis Vasilis, Rohlicek J. R. , Ostendorf M.

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/8BD8DCF4-6F56-4CA0-819D-1608076900A2
Έτος 1993
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
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Λεπτομέρειες
Βιβλιογραφική Αναφορά V. Digalakis, J. R. Rohlicek and M. Ostendorf, "ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition," IEEE Trans. Speech Audio Process., vol. 1, no. 4, pp. 431-442, Oct. 1993. doi:10.1109/89.242489 https://doi.org/10.1109/89.242489
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Περίληψη

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

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