Fast, non-iterative estimation of Hidden Markov models
1998 (English)In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Seattler, WA, USA, 1998, Vol. 4, no Piscataway, NJ, United States, 2253-2256 p.Conference paper (Refereed)
The solution of many important signal processing problems depends on the estimation of the parameters of a Hidden Markov Model (HMM). Unfortunately, to date the only known methods for performing this estimation have been iterative, and therefore computationally demanding. By way of contrast, this paper presents a new fast and non-iterative method that utilizes certain recent 'state spaced subspace system identification' (4SID) ideas from the control theory literature. A short simulation example presented here indicates this new technique to be almost as accurate as Maximum-Likelihood estimation, but an order of magnitude less computationally demanding than the Baum-Welch (EM) algorithm.
Place, publisher, year, edition, pages
Seattler, WA, USA, 1998. Vol. 4, no Piscataway, NJ, United States, 2253-2256 p.
, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. Part 1 (of 6)
Algorithms, Computer simulation, Identification (control systems), Markov processes, Mathematical models, Parameter estimation, Probability, Problem solving, Baum-Welch algorithm, Hidden Markov model, State spaced subspace system identification, Signal processing
Control Engineering Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-60583DOI: 10.1109/ICASSP.1998.681597OAI: oai:DiVA.org:kth-60583DiVA: diva2:479515
NR 201408052012-01-172012-01-132012-01-17Bibliographically approved