Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/8BD8DCF4-6F56-4CA0-819D-1608076900A2 | ||
| Year | 1993 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | 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 | ||
| Appears in Collections |
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
Copyright © DIAS 2013
