A Study on High-Order Hidden Markov Models and Applications to Speech Recognition
We propose high-order hidden Markov models (HO-HMM) to capture the duration and dynamics of speech signal. In the proposed model, both the state transition probability and the output observation probability depend not only on the current state but also on several previous states. An extended Viterbi algorithm was developed to train model and recognize speech. The performance of the HO-HMM was investigated by conducting experiments on speaker independent Mandarin digits recognition. From the experimental results, we find that as the order of HO-HMM increases, the number of error reduces. We also find that systems with both high-order state transition probability distribution and output observation probability distribution outperform systems with only high-order state transition probability distribution.
KeywordsHide Markov Model Speech Recognition Automatic Speech Recognition State Transition Probability Maximum Likelihood Linear Regression
Unable to display preview. Download preview PDF.
- 3.Russell, M.J., Cook, A.: Experimental evaluation of duration modeling techniques for automatic speech recognition. In: Proc. IEEE ICASSP, pp. 2376–2379 (1987)Google Scholar
- 4.Furui, S.: Speaker independent isolated word recognition using dynamic features of speech spectrum. IEEE Trans. Acoust., Speech, Signal Processing, 52–59 (1986)Google Scholar
- 6.Gales, M.J.F.: Maximum Likelihood Linear Transformations for HMM-based Speech Recognition, Tech. Report, CUED/FINFENG/TR291, Cambridge Univ. (1997)Google Scholar
- 7.Bahl, L.R., de Souza, P.V., Gopalakrishnan, P.S., Nahamoo, D., Picheny, M.A.: Decision Trees for Phonological Rules in Continuous Speech. In: Proc. of the IEEE ICASSP, Toronto, Canada, pp. 185–188 (1991)Google Scholar
- 9.du Preez, J.A.: Algorithms for high order hidden Markov modeling. In: Proceedings of the IEEE South African Symposium on Communications and Signal Processing, September 9-10, pp. 101–106 (1997)Google Scholar
- 11.Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Transactions on Communications, 702–710 (1980)Google Scholar
- 12.He, Y.: Extended Viterbi algorithm for second-order hidden Markov process. In: Proceedings of the IEEE 9th International Conference on Pattern Recognition, pp. 718–720 (1988)Google Scholar