Talk
Abstract:
Semiparametric Filtering in Speech Processing
Benjamin Kedem
University
of Maryland
bnk@math.umd.edu
http://www.math.umd.edu/~bnk/
Consider the following problem. You have m instruments, one
"good" and m-1 "bad," all collecting samples from the same quantity;
e.g. from a time series of the utterance "speech." We think
of the "bad" as a distortion of the "good." Suppose we have
data from all the instruments. How can we COMBINE ALL THE DATA
"good" and "bad" to improve upon the "good?" To answer this,
we turn to the following statistical formulation.
Consider m probability distributions where the first m-1 are
obtained by multiplicative exponential distortions of the the
mth distribution, it being the "good" reference or common factor.
Given m corresponding samples, "good" and "bad," we solve the
semiparametric large sample problem regarding the estimation
from the COMBINED data of each distortion and the common factor,
and testing the hypothesis that the distributions are identical.
In retrospect, the approach is a generalization of the classical
one way analysis of variance. A power comparison with the t
and F tests obtained by simulation points to the merit of the
present approach.
Material
from talk pdf
(259KB) postscript
(653KB)
Mathematical
Foundations of Speech Processing and Recognition
2000-2001
Program: Mathematics in Multimedia
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