Symbolic representations of prosodic events have been shown to be useful for spoken language applications such as speech recognition. However, a major drawback with categorical prosody models is their lack of scalability due to the difficulty in annotating large corpora with prosodic tags for training. In this paper, we present a novel, unsupervised adaptation technique for bootstrapping categorical prosodic language models (PLMs) from a small, annotated training set. Our experiments indicate that the adaptation algorithm significantly improves the quality and coverage of the PLM. On a test set derived from the Boston University Radio News corpus, the adapted PLM gave a relative improvement of 13.8% over the seed PLM on the binary pitch accent detection task, while reducing the OOV rate by 16.5% absolute.
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