Stochastic modeling of pause insertion using context-free grammar

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