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High-order hidden Markov model for piecewise linear processes and applications to speech recognition
Dempster, A. , Laird, M. , and Rubin, D. (1977). “ Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc. 39(1), 1–38.
Deng, L. , Aksmanovic, M. , Sun, X. , and Wu, C. F. J. (1994). “ Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states,” IEEE Trans. Speech Audio Process. 2, 507–520.
Ferguson, J. D. (1980). “ Variable duration models for speech,” in Symposium on the Application of Hidden Markov Models to Text and Speech, Institute for Defense Analyses, Princeton, NJ, pp. 143–179.
Gu, H.-Y. , Tseng, C.-Y. , and Lee, L.-S. (1991). “ Isolated-utterance speech recognition using hidden Markov models with bounded state durations,” IEEE Trans. Sign. Process. 39, 1743–1752.
Lee, L.-M. (2011). “ High-order hidden Markov model and application to continuous Mandarin digit recognition,” J. Inf. Sci. Eng. 27, 1919–1930.
Lee, L.-M. (2015a). “ Duration high-order hidden Markov models and training algorithms for speech recognition,” J. Inf. Sci. Eng. 31, 799–820.
Lee, L.-M. (2015b). “ Piecewise linear high-order hidden Markov models and applications to speech recognition,” in International Conference on Machine Learning and Cybernetics, pp. 383–388.
Lee, L.-M. , and Jean, F.-R. (2014). “ Model adaptation method for recognition of speech with missing frames,” J. Acoust. Soc. Am. 135, EL166–EL171.
Lee, L.-M. , and Lee, J.-C. (2006). “ A study on high-order hidden Markov models and applications to speech recognition,” in Advances in Applied Artificial Intelligence, edited by M. Ali and R. Dapoigny ( Springer, Berlin), pp. 682–690.
Mari, J. , Haton, J. , and Kriouile, A. (1997). “ Automatic word recognition based on second-order hidden Markov models,” IEEE Trans. Speech Audio Process. 5, 22–25.
Ostendorf, M. , and Roukos, S. (1989). “ A stochastic segment model for phoneme-based continuous speech recognition,” IEEE Trans. Acoust. Speech Sign. Process. 37, 1857–1869.
Rabiner, L. R. (1989). “ A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE 77, 257–286.
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The hidden Markov
models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov
model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov
model for piecewise linear processes is proposed to better approximate the behavior of a real process. A parameter estimation method based on the expectation-maximization algorithm was derived for the proposed model. Experiments on speech recognition of noisy Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate compared to a baseline hidden Markov
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