Method and apparatus for training Hidden Markov Model

Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition

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704255, 704245, G10L 506

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058901140

ABSTRACT:
HMM training method comprising a first parameter predicting step, a centroid state set calculating step, a reconstructing step, a second parameter predicting step and a control step. In the first parameter predicting step, a parameter of an HMM (hidden Markov model) is predicted based on training data. In the centroid state set calculating step, a centroid state set is calculated by clustering the state of said HMM whose parameter is predicted in the first parameter predicting step. In the reconstructing step, an HMM is reconstructed with using the centroid state calculated in the centroid state set calculating step. In the second parameter predicting step, predicted a parameter of the HMM reconstructed in the reconstructing step with using the training data. And, the centroid step is reexecuted by the control step in the case that a likelihood of the HMM whose parameter is predicted in the second parameter predicting step does not satisfy a predetermined condition.

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Levinson, S.E., Rabiner, L.R., Sondhi, M.M., An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition, 1983. The Bell System Technical Journal, vol. 62, No. 4, Apr. 1983.

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