Talk
Abstract:
Recognize Speech v/s Wreck a Nice Beach:
The Mathematics of Automatic Speech Recognition
Sanjeev Khudanpur
Center for Language and Speech
Processing
Johns
Hopkins University
sanjeev@clsp.jhu.edu
From Startrek to Star Wars and through much of science fiction,
seamlessness is a recurrent theme in human computer interfaces
-- communicating with machines the way we communicate with other
human beings. Thanks to advances in the last two decades, this
vision is closer to reality than one may suspect. Yet, we are
not around the corner from a day when an automated agent participates
at a conference table by taking notes and digging out facts
from a database in response to spoken cues. This talk focuses
on the speech recognition aspect of human computer interaction.
This introductory presentation will begin with an overview of
the evolution and the state of the art in automatic speech recognition.
It will then illustrate the application of statistical modeling,
optimization techniques and abstract algebra in transforming
what was perceived as a pipe dream in the early seventies into
a dictation system available today on a personal computer for
$99 plus taxes. Classification and regression trees, hidden
Markov models, multivariate Gaussian distributions, nonparametric
estimation and finite state automata theory are but a few of
the keystones in this ongoing march to success.
While it is only a matter of time before products employing
speech recognition will be ubiquitous as the telephone, several
challenging problems remain in this field. While the rest of
the workshop will dwell in depth upon many of these problems,
this presentation will serve to familiarize the mathematicians
in the audience with engineering aspects of automatic speech
recognition.
Mathematical
Foundations of Speech Processing and Recognition
2000-2001
Program: Mathematics in Multimedia
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