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Adaptive Statistical Utterance Phonetization for French

Gwénolé Lecorvé 1 Damien Lolive 1 
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Traditional utterance phonetization methods concatenate pronunciations of uncontextualized constituent words. This approach is too weak for some languages, like French, where transitions between words imply pronunciation modifications. Moreover, it makes it difficult to consider global pronunciation strategies, for instance to model a specific speaker or a specific accent. To overcome these problems, this paper presents a new original phonetization approach for French to generate pronunciation variants of utterances. This approach offers a statistical and highly adaptive framework by relying on conditional random fields and weighted finite state transducers. The approach is evaluated on a corpus of isolated words and a corpus of spoken utterances.
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Submitted on : Friday, July 3, 2015 - 10:59:00 AM
Last modification on : Tuesday, October 19, 2021 - 11:58:58 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 10:38:09 PM


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  • HAL Id : hal-01109757, version 1


Gwénolé Lecorvé, Damien Lolive. Adaptive Statistical Utterance Phonetization for French. Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. 5 p., 2 columns. ⟨hal-01109757⟩



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