Pierre Lanchantin


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Automatic Phoneme Segmentation with Relaxed Textual Constraints
Pierre Lanchantin | Andrew C. Morris | Xavier Rodet | Christophe Veaux
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Speech synthesis by unit selection requires the segmentation of a large single speaker high quality recording. Automatic speech recognition techniques, e.g. Hidden Markov Models (HMM), can be optimised for maximum segmentation accuracy. This paper presents the results of tuning such a phoneme segmentation system. Firstly, using no text transcription, the design of an HMM phoneme recogniser is optimised subject to a phoneme bigram language model. Optimal performance is obtained with triphone models, 7 states per phoneme and 5 Gaussians per state, reaching 94.4% phoneme recognition accuracy with 95.2% of phoneme boundaries within 70 ms of hand labelled boundaries. Secondly, using the textual information modeled by a multi-pronunciation phonetic graph built according to errors found in the first step, the reported phoneme recognition accuracy increases to 96.8% with 96.1% of phoneme boundaries within 70 ms of hand labelled boundaries. Finally, the results from these two segmentation methods based on different phonetic graphs, the evaluation set, the hand labelling and the test procedures are discussed and possible improvements are proposed.

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IrcamCorpusTools : plate-forme pour les corpus de parole [IRCAM Corpus Tools: Managing speech corpora]
Grégory Beller | Christophe Veaux | Gilles Degottex | Nicolas Obin | Pierre Lanchantin | Xavier Rodet
Traitement Automatique des Langues, Volume 49, Numéro 3 : Recherches actuelles en phonologie et en phonétique : interfaces avec le traitement automatique des langues [Current Research in Phonology and Phonetics: Interfaces with Natural-Language Processing]