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Context Independent And Context Dependent Hybrid HMM/ANN Systems For Vocabulary Independent Tasks
| Citations: | 3 - 1 self |
BibTeX
@MISC{Dupont_contextindependent,
author = {S. Dupont and C. Ris and O. Deroo and V. Fontaine and J. M. Boite and L. Zanoni},
title = {Context Independent And Context Dependent Hybrid HMM/ANN Systems For Vocabulary Independent Tasks},
year = {}
}
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Abstract
In this paper, hybrid HMM/ANN systems are used to model context dependent phones. In order to reduce the number of parameters as well as to better catch the dynamics of the phonetic segments, we combine (context dependent) diphone models with context independent phone models. Transitions from phone to phone are modeled as generalized context dependent distributions while phonetic units are context independent models trained on the less coarticulated middle part of each phone. Words are thus modeled as a sequence of probability distributions alternatively representing the middle part of the phonemes and the transitions from phone to phone. A single neural network is used to estimate both context independent phone probabilities and generalized context dependent diphone (phone to phone transition) probabilities. Resulting systems are compared to classical context independent phone-based HMM/ANN systems with the same number of parameters. The Phonebook isolated word database has been used ...
Keyphrases
context independent vocabulary independent task single neural network context dependent phonetic unit context dependent diphone context dependent phone word database generalized context dependent distribution middle part probability distribution context independent model phone transition context independent phone probability hybrid hmm ann system phonetic segment coarticulated middle part context independent phone model diphone model
