Go to main content

PDF

Description

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.

Details

Files

Statistics

from
to
Export
Download Full History
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS