Seminar on Computational Learning and
Adaptation
Theory and Application of
Discriminative Hidden Markov Model Training
Sadik Kapadia
Motorola
Human Interface Lab
Palo Alto, CA
Sadik.Kapadia@motorola.com
Although discriminative training of hidden Markov models has been
attempted for many years, success has been isolated and intermittent.
The Baum-Welch "maximum likelihood" algorithm remains the training
method of choice. In this talk I shall show that this puzzling state
of affairs is due to some fundamental misunderstandings about the
training problem. We show that "maximum likelihood" is well founded,
and there exist discriminative formulations (e.g., maximum mutual
information and frame discrimination) that enjoy some advantages.
Experimental results using these techniques will be presented.
Date: Thurs., Jan 11
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Time: 4:15-5:30PM
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Place: Cordura 100
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