# 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