Chinese Prosodic Phrasing with the Source-Channel Model
The prosodic phrasing is a classic problem in nature language process, which is not only useful for text-tospeech(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.
Prosodic phrasing Source-Channel Model HMM Rhythm Model
Honghui Dong Yong Qin Limin Jia
State Key Laboratory of Rail Trafic Control and Safety, Beijing Jiaotong University, Beijing 100044
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
6168-6171
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）