Talk
Abstract:
Parameter Adaptation and Compensation in Designing Maximum A
Posteriori Decision Rules for Automatic Speech Recognition
Chin-Hui Lee
Dialogue
Systems Research Department
Bell Laboratories, Lucent Technologies
chl@research.bell-labs.com
Recent advances in automatic speech recognition are mainly accomplished
by designing a plug-in maximum a posteriori decision rule such
that the forms of the acoustic and language model distributions
are specified and the parameters of the assumed distributions
are estimated from a collection of speech and language training
examples. Maximum likelihood point estimation of decision parameters
is by far the most prevailing training method. However, due
to the problems of unknown speech and language distributions,
sparse training data, high spectral and temporal variabilities
in speech, and possible mismatch between training and testing
conditions, a dynamic learning strategy is needed. In this talk,
the mathematical foundations of Bayesian adaptation of decision
parameters is first described. Maximum a posteriori point estimation
is then developed. Finally robust decision rules for compensating
mismatch are formulated and discusseds.
Material
from talks pdf
(207KB) postscript
(256KB)
Mathematical
Foundations of Speech Processing and Recognition
2000-2001
Program: Mathematics in Multimedia
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