ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognitionML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-021993enA 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 speechhttp://creativecommons.org/licenses/by/4.0/IEEE Transactions on Speech and Audio Processing14431-442 Digalakis Vasilis Διγαλακης Βασιλης Rohlicek J. R. Ostendorf M. Institute of Electrical and Electronics Engineers Speech recognition