aas@brolga.cc.uq.oz.au (Alex Sergejew) (05/02/91)
The header pretty much says it all. I'm aware that there is an extensive literature on efficiently estimating state transitions and unknown parameters of hidden and semi markov signal models from time series data, primarily used in the speech processing context, but have been making heavy weather of translating the basic references I've found into useful working code. Looking in the usual places (netlib, Numerical recipes, archie, etc) has drawn a blank, and all the speech processing types I've been able to ask seem not to have code or algorithms they deem fit for sharing. Could anyone please point me in the right direction, whether it be algorithmic descriptions or (preferably) PD source code. If there is enough interest I will gladly post a summary. Alex. _--_|\ Alex A Sergejew Internet: aas@cc.uq.OZ.AU / X University of Queensland Voice: +61-7-271-8298 \_.--._/ Brisbane, Qld, Australia Fax: +61-7-271-8567
aas@brolga.cc.uq.oz.au (Alex Sergejew) (05/26/91)
Gidday! Back on Thu, 2 May 1991 11:34:25 GMT I asked the net: > I'm aware that there is an extensive literature on efficiently estimating > state transitions and unknown parameters of hidden and semi markov signal > models from time series data, primarily used in the speech processing > context, but have been making heavy weather of translating the basic > references I've found into useful working code. > > Could anyone please point me in the right direction, whether it be > algorithmic descriptions or (preferably) PD source code. If there is enough > interest I will gladly post a summary. The following were also interested: David Haines <haines%hobart@cs.umass.edu> Robert Wylie <wylie@watnow.uwaterloo.ca> Krishna Nanda <nanda@linc.cis.upenn.edu> Sven.Koenig@a.gp.cs.cmu.edu Wolfgang Nejdl <vexpert!nejdl@relay.eu.net> Rick@vee.lrz-muenchen.de and my thanks to the following whose replies, whilst pointing to helpful references (most of which I was already aware of), unfortunately did not pin down any working code: Mou-Yen Chen <mychen@cs.buffalo.edu> Len Moskowitz <moskowit@paul.rutgers.edu> Robert Goldman <rpg@rex.cs.tulane.edu> I append extracts from their replies. I have informally been reassured that semi Markov and HMM algorithms are not all that difficult in themselves, once one gets past the notation in which they are described. If anyone *does* have more information (or code) I'm sure we'd all love to hear about it! Alex. _--_|\ Alex A Sergejew Internet: aas@cc.uq.OZ.AU / X University of Queensland Voice: +61-7-271-8298 \_.--._/ Brisbane, Qld, Australia Fax: +61-7-271-8567 +-+-+-+-+-+-+-+ >From: Mou-Yen Chen <mychen@cs.buffalo.edu> >Message-Id: <9105030737.AA20871@sirius.cs.buffalo.edu> >In-Reply-To: <1991May2.113425.802@brolga.cc.uq.oz.au> >Organization: CEDAR, SUNY at Buffalo We are using HMM(Hidden Markov Model) in hand-written word recognition. As my knowledge, the direct way to implement MM is the Viterbi Algorithm which is the statistical version of DP(Dynamic Programming). Maybe these references can help you a little: (1) A. Kundu, Yang He and P. Bahl, "Recognition of Handwritten Word: First and Second Order Hidden Markov Model Based Approach", Pattern Recognition, Vol. 22, No. 3, pp.283-297, 1989 (2) L.R. Rabiner and B.H. Juang, "An Introduction to Hidden Markov Model", IEEE ASSP Magazine, Vol. 3, No. 1, Jan.1986, pp.4-16 (3) G. David Forney, JR. "The Viterbi Algorithm", Proc. IEEE, Vol.61, No.3, March 1973. +-+-+-+-+-+-+-+ >From: Len Moskowitz <moskowit@paul.rutgers.edu> >Message-Id: <9105031826.AA17586@paul.rutgers.edu> >In-Reply-To: USENET article <1991May2.113425.802@brolga.cc.uq.oz.au> It's not Markov Models but there was some work on something similar that's embodied in a product called AIM (Abductory Induction Mechanism) available from AbTech Corp (700 Harris St., Charlottesville, VA 22901 USA, phone: 804-977-0686). +-+-+-+-+-+-+-+ >From: Robert Goldman <rpg@rex.cs.tulane.edu> >Message-Id: <9105032022.AA21658@rex.cs.tulane.edu> >In-Reply-To: aas@brolga.cc.uq.oz.au's message of 2 May 91 11:34:25 GMT ... the best presentation of the Viterbi algorithm I've found for presentation to students is the survey article by Forney, as follows: @ARTICLE{Forney, AUTHOR = {G. David Forney}, Title = {The Viterbi Algorithm}, JOURNAL = {Proceedings of the IEEE}, YEAR = {1973}, VOLUME = {61}, NUMBER = {3}, PAGES = {268--278} } I have not found a comparably detailed (i.e., with pseudo-Algol) treatment of the forward-backward algorithm for HMMs. +-+-+-+-+-+-+-+