Stochastic modeling of pause insertion using context-free grammar

Shigeru Fujio*, Yoshinori Sagisaka, Norio Higuchi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


In this paper, we propose a model for predicting pause insertion using a stochastic context-free grammar (SCFG) for an input part of speech sequence. In this model, word attributes and stochastic phrasing information obtained by a SCFG trained using phrase dependency bracketings and bracketings based on pause locations are used. Using the Inside-Outside algorithm for training, corpora with phrase dependency brackets are first used to train the SCFG from scratch. Next, this SCFG is re-trained using the same corpora with bracketings based on pause locations. Then, the probabilities of each bracketing structure are computed using the SCFG, and these are used as parameters in the prediction of the pause locations. Experiments were carried out to confirm the effectiveness of the stochastic model for the prediction of pause locations. In test with open data, 85.2% of the pause boundaries and 90.9% of the no-pause boundaries were correctly predicted.

Original languageEnglish
Pages (from-to)604-607
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 1 (of 5) - Detroit, MI, USA
Duration: 1995 May 91995 May 12

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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