A new maximum likelihood gradient algorithm for on-line hidden Markov model identification

Lain B. Collings; Tobias Ryden

doi:10.1109/ICASSP.1998.681599

A new maximum likelihood gradient algorithm for on-line hidden Markov model identification

Lain B. Collings, Tobias Ryden

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

17 Citations (Scopus)

Abstract

This paper presents a new algorithm for on-line identification of hidden Markov model (HMM) parameters. The scheme is gradient based, and provides parameter estimates which recursively maximise the likelihood function. It is therefore a recursive maximum likelihood (RML) algorithm, and it has optimal asymptotic properties. The only current on-line HMM identification algorithm with anything other than suboptimal rate of convergence is based on a prediction error (PE) cost function. As well as presenting a new algorithm, this paper also highlights and explains a counter-intuitive convergence problem for the current recursive PE (RPE) algorithm, when operating in low noise conditions. Importantly, this problem does not exist for the new RML algorithm. Simulation studies demonstrate the superior performance of the new algorithm. compared to current techniques.

Original language	English
Title of host publication	Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
Place of Publication	Piscataway, N.J.
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2261-2264
Number of pages	4
Volume	6
ISBN (Electronic)	0780344308, 0780344316
ISBN (Print)	0780344286, 0780344294
DOIs	https://doi.org/10.1109/ICASSP.1998.681599
Publication status	Published - May 1998
Externally published	Yes
Event	1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - 1998 - Seattle, United States Duration: 12 May 1998 → 15 May 1998

Other

Other	1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - 1998
Country/Territory	United States
City	Seattle
Period	12/05/98 → 15/05/98

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10.1109/ICASSP.1998.681599

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Collings, L. B., & Ryden, T. (1998). A new maximum likelihood gradient algorithm for on-line hidden Markov model identification. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 (Vol. 6, pp. 2261-2264). [681599] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.1998.681599

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Collings, LB & Ryden, T 1998, A new maximum likelihood gradient algorithm for on-line hidden Markov model identification. in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998. vol. 6, 681599, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, N.J., pp. 2261-2264, 1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - 1998, Seattle, Washington, United States, 12/05/98. https://doi.org/10.1109/ICASSP.1998.681599

A new maximum likelihood gradient algorithm for on-line hidden Markov model identification. / Collings, Lain B.; Ryden, Tobias.

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998. Vol. 6 Piscataway, N.J. : Institute of Electrical and Electronics Engineers (IEEE), 1998. p. 2261-2264 681599.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - This paper presents a new algorithm for on-line identification of hidden Markov model (HMM) parameters. The scheme is gradient based, and provides parameter estimates which recursively maximise the likelihood function. It is therefore a recursive maximum likelihood (RML) algorithm, and it has optimal asymptotic properties. The only current on-line HMM identification algorithm with anything other than suboptimal rate of convergence is based on a prediction error (PE) cost function. As well as presenting a new algorithm, this paper also highlights and explains a counter-intuitive convergence problem for the current recursive PE (RPE) algorithm, when operating in low noise conditions. Importantly, this problem does not exist for the new RML algorithm. Simulation studies demonstrate the superior performance of the new algorithm. compared to current techniques.

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Collings LB, Ryden T. A new maximum likelihood gradient algorithm for on-line hidden Markov model identification. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998. Vol. 6. Piscataway, N.J.: Institute of Electrical and Electronics Engineers (IEEE). 1998. p. 2261-2264. 681599 https://doi.org/10.1109/ICASSP.1998.681599

A new maximum likelihood gradient algorithm for on-line hidden Markov model identification

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