Abstract

While the current TTS systems can deliver quite acceptable segmental quality of synthesized speech for voice user interface applications, its prosody is still perceived by users as “robotic” or not expressive. In this paper, we investigate how to improve TTS prosody prediction and detection. Conditional Random Field (CRF), a discriminative probabilistic model for the labeling the sequential data, is adopted. Rich syntactic and acoustic, contextual features are used in building the CRF models. Experiments performed on Boston University Radio Speech Corpus show that CRF models trained on our proposed rich contextual features can improve the accuracy of prosody prediction and detection in both speaker-dependent and speaker-independent cases. The performance is either comparable or better than the best reported results.

Metadata

publication

2010 7th International Symposium on Chinese Spoken Language Processing, 135-138, 2010

year

2010

publication date

2010/11/29

authors

Yao Qian, Zhizheng Wu, Xuezhe Ma, Frank Soong

link

https://ieeexplore.ieee.org/abstract/document/5684835/

resource_link

http://www.cs.cmu.edu/~xuezhem/publications/ISCSLP2010.pdf

conference

2010 7th International Symposium on Chinese Spoken Language Processing

pages

135-138

publisher

IEEE