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Search and learning strategies for improving hidden Markov models

โœ Scribed by Renato De Mori; Michael Galler; Fabio Brugnara

Publisher
Elsevier Science
Year
1995
Tongue
English
Weight
121 KB
Volume
9
Category
Article
ISSN
0885-2308

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โœฆ Synopsis

A speaker-independent automatic speech recognition system is developed using hidden Markov models (HMMs). Simulated annealing and randomized search are used to optimize discrete features of the system, including topologies, parameter ties, context clusters, and the sizes of mixture densities. Domain knowledge is used to initialize and to constrain the search, which optimizes recognition performance while reducing the number of model parameters. System performance results for new types of discrete and continuous HMMs measured on the TIMIT corpus are reported. The small set of context-independent phoneme HMMs produced is competitive with much larger systems of context-dependent models.

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