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Multiple Pronunciation Generation using Grapheme-to-Phoneme Conversion based on Conditional Random Fields

Irina Illina 1 Dominique Fohr 1 Denis Jouvet 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We propose an approach to grapheme-to-phoneme conversion with multiple pronunciations based on a probabilistic method: Conditional Random Fields (CRF). CRF give a long term prediction and assume relaxed state independence condition compared to HMMs. Moreover, we propose an algorithm to one-to-one letter to phoneme alignment needed for CRF training. This alignment is based on discrete HMM. This paper investigated the impact of the training set size and the multiple pronunciation generation. Validated on BDLex French dictionary, our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models in term of the quality of the pronunciations and in term of recall and precision measures for multiple pronunciation variants generation.
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Conference papers
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https://hal.inria.fr/inria-00616325
Contributor : Denis Jouvet Connect in order to contact the contributor
Submitted on : Monday, August 22, 2011 - 10:34:21 AM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM

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  • HAL Id : inria-00616325, version 1

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CNRS | INRIA | LORIA | UNIV-LORRAINE

Citation

Irina Illina, Dominique Fohr, Denis Jouvet. Multiple Pronunciation Generation using Grapheme-to-Phoneme Conversion based on Conditional Random Fields. XIV International Conference "Speech and Computer" (SPECOM'2011), Sep 2011, Kazan, Russia. ⟨inria-00616325⟩

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