Pronunciation variation generation for spontaneous speech synthesis using state-based voice transformation

Chung Han Lee, Chung-Hsien Wu, Jun Cheng Guo

研究成果: Conference contribution

13 引文 斯高帕斯(Scopus)

摘要

This study presents an approach to Hidden Markov Models (HMM)-based spontaneous speech synthesis with pronunciation variation for better spontaneity. Pronunciation variation generally occurs in spontaneous speech and plays an important role in expressing the spontaneity. In this study, a state-based transformation function is adopted to model the relation between read speech and the corresponding spontaneous speech with pronunciation variations. The transformation function is then used to generate the state-based pronunciation variations. Due to the lack of training data, the articulatory features are used to cluster the transformation functions using Classification and Regression Trees (CARTs) such that the unseen pronunciation variation with the same articulatory features can be generated from the transformation function in the same cluster. Objective and subjective tests are conducted to evaluate the performance of the proposed approach. The experimental results show that the proposed transformation function achieves a significant improvement on spontaneity in synthesized speech.

原文English
主出版物標題2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
頁面4826-4829
頁數4
DOIs
出版狀態Published - 2010 十一月 8
事件2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
持續時間: 2010 三月 142010 三月 19

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
國家United States
城市Dallas, TX
期間10-03-1410-03-19

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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