LAMP Seminar
Language and Media Processing Laboratory
Conference Room 4406
A.V. Williams Building
University of Maryland

Tutorial on Hidden Markov Models: PART I
Tapas Kanungo
Monday, March 16 1998, 4PM

In many recognition problems we are required to discover the underlying structure in a sequence of observed symbols. Few example problem areas are: speech recognition, optical character recognition, part-of-speech disambiguation,gesture recognition from video sequences and finding structural
motifs in DNA seqences.

A mathematical formulation that has been successfully applied to attack such problems is hidden Markov model (HMM). My talk
will be a tutorial on HMMs and is based on Rabiner's paper: "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Prov. of IEEE, vol. 77, no. 2, pp. 257-286, 1989.

Topics that I will be cover in my talk are:
-- The hidden Markov model formulation
-- Forward algorithm for estimating the total probability of a model, given a sequence of observed symbols;
-- Viterbi algorithm for estimating the most likely state sequence, given a sequence of observed symbols and a model;
-- Baum-Welch algorithm for estimating the model parameters, given a sequence of observed symbols.

I will cover the first two topics in the first talk, and the last two in the second talk. The date and time of the second talk will be announced later.

I have a software implementation of all the algorithms. If you are interested in getting a copy, please send me email at

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