Automatic prosodic segmentation by F₀ clustering using superpositional modeling

In this paper, we propose an automatic method for detecting accent phrase boundaries in Japanese continuous speech by using F₀ information. In the training phase, hand labeled accent patterns are parameterized according to a superpositional model proposed by Fujisaki, and assigned to some clusters by a clustering method, in which accent templates are calculated as centroid of each cluster. In the segmentation phase, automatic N-best extraction of boundaries is performed by One-Stage DP matching between the reference templates and the target F₀ contour. About 90% of accent phrase boundaries were correctly detected in speaker independent experiments with the ATR Japanese continuous speech database.