Summary of the paper

Title Towards Optimal TTS Corpora
Authors Didier Cadic, Cédric Boidin and Christophe d'Alessandro
Abstract Unit selection text-to-speech systems currently produce very natural synthesized phrases by concatenating speech segments from a large database. Recently, increasing demand for designing high quality voices with less data has created need for further optimization of the textual corpus recorded by the speaker. This corpus is traditionally the result of a condensation process: sentences are selected from a reference corpus, using an optimization algorithm (generally greedy) guided by the coverage rate of classic units (diphones, triphones, words…). Such an approach is, however, strongly constrained by the finite content of the reference corpus, providing limited language possibilities. To gain flexibility in the optimization process, in this paper, we introduce a new corpus building procedure based on sentence construction rather than sentence selection. Sentences are generated using Finite State Transducers, assisted by a human operator and guided by a new frequency-weighted coverage criterion based on Vocalic Sandwiches. This semi-automatic process requires time-consuming human intervention but seems to give access to much denser corpora, with a density increase of 30 to 40% for a given coverage rate.
Topics Speech Synthesis, Corpus (creation, annotation, etc.), Speech resource/database
Full paper Towards Optimal TTS Corpora
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Bibtex @InProceedings{CADIC10.608,
  author = {Didier Cadic and Cédric Boidin and Christophe d'Alessandro},
  title = {Towards Optimal TTS Corpora},
  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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