A maximum entropy approach to natural language processing
Computational Linguistics
A hierarchical stochastic model for automatic prediction of prosodic boundary location
Computational Linguistics
Integrating linguistic and performance-based constraints for assigning phrase breaks
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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The prosodic phrasing is a classic problem in nature language process, which is not only useful for text-to-speech (TTS), but for speech recognition, statistic machine learning etc.. This paper introduces and discusses the sourcechannel model for Chinese prosodic phrasing. Based on the basic idea, the Hidden Markov Model (HMM) and the improved source-channel model are both used to describe the phrasing problem. In the improved source-channel model, maximum entropy model is used, and the discriminative training is introduced. And the rhythm model is proposed to describe the property of the utterance. The phrase-length model and the foot-pattern model both are used to describe the rhythm model, respectively. The experiments show that this approach achieved a good performance for prosodic phrasing. The improved source-channel model achieve a better performance than the Hidden Markov Model. And the foot-pattern model is the better one as a rhythm model.