Summary of the paper

Title Automatic Detection of Syllable Boundaries in Spontaneous Speech
Authors Brigitte Bigi, Christine Meunier, Irina Nesterenko and Roxane Bertrand
Abstract This paper presents the outline and performance of an automatic syllable boundary detection system. The syllabification of phonemes is performed with a rule-based system, implemented in a Java program. Phonemes are categorized into 6 classes. A set of specific rules are developed and categorized as general rules which can be applied in all cases, and exception rules which are applied in some specific situations. These rules deal with a French spontaneous speech corpus. Moreover, the proposed phonemes, classes and rules are listed in an external configuration file of the tool (under GPL licence) that make the tool very easy to adapt to a specific corpus by adding or modifying rules, phoneme encoding or phoneme classes, by the use of a new configuration file. Finally, performances are evaluated and compared to 3 other French syllabification systems and show significant improvements. Automatic system output and expert's syllabification are in agreement for most of syllable boundaries in our corpus.
Topics Tools, systems, applications, Phonetic Databases, Phonology, Corpus (creation, annotation, etc.)
Full paper Automatic Detection of Syllable Boundaries in Spontaneous Speech
Slides Automatic Detection of Syllable Boundaries in Spontaneous Speech
Bibtex @InProceedings{BIGI10.219,
  author = {Brigitte Bigi and Christine Meunier and Irina Nesterenko and Roxane Bertrand},
  title = {Automatic Detection of Syllable Boundaries in Spontaneous Speech},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
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