Automatic Prosodic Segmentation by F0 Clustering Using Superpositional Modeling.

Abstract

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.