摘要
Prosodic structure generation is the key component in improving the intelligibility and naturalness of synthetic speech for a text-to-speech (TTS) system. This paper investigates the problem of automatic segmentation of prosodic word and prosodic phrase,which are two fundamental layers in the hierarchical prosodic structure of Mandarin,and presents a two-stage prosodic structure generation strategy. Conditional random fields (CRF) models are built for both prosodic word and prosodic phrase prediction at the front end with diflerent feature selections. Besides,a transformation-based error-driven learning (TBL) modification module is introduced in the back end to amend the initial prediction. Experiment results show that the approach combining CRF and TBL achieves an F-score of 94.66%.
Prosodic structure generation is the key component in improving the intelligibility and naturalness of synthetic speech for a text-to-speech （TTS） system. This paper investigates the problem of automatic segmentation of prosodic word and prosodic phrase,which are two fundamental layers in the hierarchical prosodic structure of Mandarin,and presents a two-stage prosodic structure generation strategy. Conditional random fields （CRF） models are built for both prosodic word and prosodic phrase prediction at the front end with diflerent feature selections. Besides,a transformation-based error-driven learning （TBL） modification module is introduced in the back end to amend the initial prediction. Experiment results show that the approach combining CRF and TBL achieves an F-score of 94.66%.
出处
《自动化学报》
CSCD
北大核心
2010年第11期1569-1574,共6页
Acta Automatica Sinica
基金
Supported by National Natural Science Foundation of China(90920001)
the Key Project of the Ministry of Education of China(108012)
Joint-research Project between France Telecom R&DBeijing and Beijing University of Posts and Telecommunications(SEV01100474)
关键词
中文语音合成系统
两层韵律结构生成体系
计算机技术
自动化系统
Text-to-speech （TTS）
prosodic structure generation
conditional random fields （CRF）
transformation-based errordriven learning （TBL）
