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 |
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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 | |
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 |
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Country/Territory | United States |
City | Seattle |
Period | 12/05/98 → 15/05/98 |