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.
Title
Dynamic Bayesian Networks and the Concatenation Problem in Speech Recognition
Published
1996-12-11
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
CSD-96-927
Type
Text
Extent
9 p
Archive
The Engineering Library
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