Using Weakly Supervised Learning to Improve Prosody Labeling

UWEETR-2005-0003

Author(s):
D. Wong, M. Ostendorf and J. Kahn

Keywords:
weakly supervised learning, prosody, conversational speech, prosodic breaks, prominence, pitch accent, EM training, decision tree, co-training, self-training, bagging

Abstract

Automatic annotation of prosodic events could help improve speech understanding and synthesis. However, little annotated data is available for training prosody models because hand-labeling is prohibitively expensive. To address this issue, we explore weakly supervised learning techniques (EM, co-training, and self-training with bagging) that use only a small amount of hand-labeled data in combination with a large unlabeled data set with syntactic parses. Experiments on conversational speech show improved performance of decision trees on labeling symbolic prosodic events, specifically break indices and pitch accents.

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