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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.

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URIhttp://purl.tuc.gr/dl/dias/8BD8DCF4-6F56-4CA0-819D-1608076900A2-
Identifierhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=242489&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel4%2F89%2F6235%2F00242489.pdf%3Farnumber%3D242489-
Identifierhttps://doi.org/10.1109/89.242489-
Languageen-
Extent12 pagesen
TitleML estimation of a stochastic linear system with the EM algorithm and its application to speech recognitionen
CreatorDigalakis Vasilisen
CreatorΔιγαλακης Βασιληςel
CreatorRohlicek J. R. en
CreatorOstendorf M.en
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryA 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 speechen
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-02-
Date of Publication1993-
SubjectSpeech recognitionen
Bibliographic CitationV. 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:10.1109/89.242489en

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