A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Automatic grammar induction and parsing free text: a transformation-based approach
HLT '93 Proceedings of the workshop on Human Language Technology
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
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Predicting prosodic words boundaries will directly influence the naturalness of synthetic speech, because prosodic word is at the lowest level of prosody hierarchy. In this paper, a Chinese prosodic phrasing method based on CRF and TBL model is proposed. First a CRF model is trained to predict the prosodic words boundaries from lexicon words. After that we apply a TBL based error driven learning approach to refine the results. The experiments shows that this joint method performs much better than HMM.