Speech generation systems can benefit from the prediction of abstract prosodic labels from text input. Earlier methods of prosodic label prediction have relied on hand-written rules or on statistical methods such as decision trees. Statistical methods have the advantage of being automatically trainable and are portable to new domains. This research presents a new method for automatically training an abstract prosodic label predictor, transformational rule-based learning. This method is automatically trainable. Results will be presented for pitch accent location and phrase boundary prediction.
Cite as: Fordyce, C.S., Ostendorf, M. (1998) Prosody prediction for speech synthesis using transformational rule-based learning. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0682, doi: 10.21437/ICSLP.1998-7
@inproceedings{fordyce98_icslp,
author={Cameron S. Fordyce and Mari Ostendorf},
title={{Prosody prediction for speech synthesis using transformational rule-based learning}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0682},
doi={10.21437/ICSLP.1998-7}
}