Abstract
This chapter presents three prosodic recognition models which are capable of resolving syntactic ambiguities using acoustic features measured from the speech signal. The models are based on multi-variate statistical techniques that identify a linear relationship between sets of acoustic and syntactic features. One of the models requires hand-labelled break indices for training and achieves up to 76% accuracy in resolving syntactic ambiguities on a standard corpus. The other two prosodic recognition models can be trained without any prosodic labels. These prosodically unsupervised models achieve recognition accuracy of up to 74%. This result suggests that it may be possible to train prosodic recognition models for very large speech corpora without requiring any prosodic labels.
Research was carried out while affiliated with the Speech Technology Research Group, University of Sydney and ATR Interpreting Telecommunications Research Labs.
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Hunt, A.J. (1997). Training Prosody-Syntax Recognition Models without Prosodic Labels. In: Sagisaka, Y., Campbell, N., Higuchi, N. (eds) Computing Prosody. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2258-3_20
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DOI: https://doi.org/10.1007/978-1-4612-2258-3_20
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