Hidden conditional random fields with M-to-N alignments for grapheme-to-phoneme conversion

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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Hidden conditional random fields with M-to-N alignments for grapheme-to-phoneme conversion

Cited by 7 publications

References 12 publications

Literature Review of Research on Common Methods of Grapheme-To-Phoneme

Literature Review of Research on Common Methods of Grapheme-To-Phoneme

Novel two-stage model for grapheme-to-phoneme conversion using new grapheme generation rules

Adaptive statistical utterance phonetization for French

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