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