A study on high-order hidden markov models and applications to speech recognition

Authors:
Lee-Min Lee;Jia-Chien Lee
Affiliations:
Department of Electrical Engineering, Da Yeh University, Taiwan;Department of Communication Engineering, Da Yeh University, Taiwan
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

Citing 2
Cited 1

Decision trees for phonological rules in continuous speech

ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Continuously variable duration hidden Markov models for automatic speech recognition

Computer Speech and Language

A method for noise-robust context-aware pattern discovery and recognition from categorical sequences

Pattern Recognition

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Abstract

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.