Information Retrieval
Machine Learning
Transformation-based learning in the fast lane
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An RNN-based algorithm to detect prosodic phrase for Chinese TTS
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Segmenting unrestricted Chinese text into prosodic words instead of lexical words
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Prosodic boundary prediction based on maximum entropy model with error-driven modification
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
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This paper describes a rule-learning approach towards Chinese prosodic phrase prediction for TTS systems. Firstly, we prepared a speech corpus having about 3000 sentences and manually labelled the sentences with two-level prosodic structure. Secondly, candidate features related to prosodic phrasing and the corresponding prosodic boundary labels are extracted from the corpus text to establish an example database. A series of comparative experiments is conducted to figure out the most effective features from the candidates. Lastly, two typical rule learning algorithms (C4.5 and TBL) are applied on the example database to induce prediction rules. The paper also suggests general evaluation parameters for prosodic phrase prediction. With these parameters, our methods are compared with RNN and bigram based statistical methods on the same corpus. The experiments show that the automatic rule-learning approach can achieve better prediction accuracy than the non-rule based methods and yet retain the advantage of the simplicity and understandability of rule systems. Thus it is justified as an effective alternative to prosodic phrase prediction.