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Grapheme-to-Phoneme Conversion using 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 based on a probabilistic method: Conditional Random Fields (CRF). CRF give a long term prediction, assume relaxed state independence condition. Moreover, we propose an algorithm to one-to-one letter to phoneme alignment needed for CRF training. This alignment is based on discrete HMM. The proposed system is validated on two pronunciation dictionaries. Different CRF features are studied: POS-tag, context size, unigram versus bigram. Our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models for the quality of the pronunciations, but provides better recall and precision measures for multiple pronunciation variants generation.
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Contributor : Denis Jouvet Connect in order to contact the contributor
Submitted on : Wednesday, August 17, 2011 - 5:18:06 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM


  • HAL Id : inria-00614981, version 1



Irina Illina, Dominique Fohr, Denis Jouvet. Grapheme-to-Phoneme Conversion using Conditional Random Fields. 12th Annual Conference of the International Speech Communication Association - Interspeech 2011, International Speech Communication Association (ISCA) et The Italian Regional SIG - AISV (Italian Speech Communication Association), Aug 2011, Florence, Italy. ⟨inria-00614981⟩



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