Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Dynamic Bayesian Networks and the Concatenation Problem in Speech Recognition

Geoffrey Zweig

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-96-927
December 1996

http://www.eecs.berkeley.edu/Pubs/TechRpts/1996/CSD-96-927.pdf

This report describes a method for structuring dynamic Bayesian networks so that word and sentence-level models can be constructed from low-level phonetic models. This ability is a fundamental prerequisite for large-scale speech recognition systems, and is well-addressed in the context of hidden Markov models. With dynamic Bayesian networks, however, subword units cannot simply be concatenated together, and an entirely different approach is necessary.


BibTeX citation:

@techreport{Zweig:CSD-96-927,
    Author = {Zweig, Geoffrey},
    Title = {Dynamic Bayesian Networks and the Concatenation Problem in Speech Recognition},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {1996},
    Month = {Dec},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1996/5297.html},
    Number = {UCB/CSD-96-927},
    Abstract = {This report describes a method for structuring dynamic Bayesian networks so that word and sentence-level models can be constructed from low-level phonetic models. This ability is a fundamental prerequisite for large-scale speech recognition systems, and is well-addressed in the context of hidden Markov models. With dynamic Bayesian networks, however, subword units cannot simply be concatenated together, and an entirely different approach is necessary.}
}

EndNote citation:

%0 Report
%A Zweig, Geoffrey
%T Dynamic Bayesian Networks and the Concatenation Problem in Speech Recognition
%I EECS Department, University of California, Berkeley
%D 1996
%@ UCB/CSD-96-927
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/1996/5297.html
%F Zweig:CSD-96-927