Discriminative Training of Stream Weights in a Multi-Stream HMM as a Linear Programming Problem

MPhil Thesis Defence


Title: "Discriminative Training of Stream Weights in a Multi-Stream HMM as
a Linear Programming Problem"

By

Mr. Yik-Lun Ng


Abstract

Hidden Markov model (HMM) is a commonly used statistical model for pattern
classification. One way to incorporate multiple information sources under
the HMM framework is to use the multi-stream HMM. The state log likelihood
of a multi-stream HMM is usually computed as a linear combination of the
stream log-likelihoods using a set of stream weights. The estimation of
stream weights is important because it can affect the performance of the
multi-stream HMM greatly. Various estimation methods have been proposed.
Some pose the estimation of stream weights as an optimization problem and
various objective functions such as minimum classification error and
maximum entropy had been tried. In this thesis, we cast the estimation of
stream weights into a linear programming (LP) problem. The LP formulation
is very flexible, allowing various degrees of tying the stream weights. It
also de-couples the estimation of stream weights from the recognition
system so that the estimation may be done by any commonly available and
efficient LP solvers. In practice, however, we may not have complete
knowledge of the feasible region since it is constructed from a limited
number of competing hypotheses generated from the current acoustic model.
We investigate an iterative LP optimization algorithm in which additional
constraints on the parameters being optimized is further imposed. We
evaluate our LP formulation in automatic speech recognition using the
Resource Management recognition task. It is found that the stream weights
of a 4-stream HMM found via our LP formulation reduce the word error rate
(WER) of the baseline system by 17% and WER of the stream weights found by
extensive brute-force grid search by 8.75%.


Date:			Tuesday, 15 January 2008

Time:			10:00a.m.-12:00noon

Venue:			Room 3402
			Lifts 17-18

Committee Members:	Dr. Brian Mak (Supervisor)
			Dr. Dit-Yan Yeung (Chairperson)
			Dr. Huamin Qu


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