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