Mandarin Stress Detection Using Syllable-Based Acoustic and Syntactic Features

摘要：

Automatic stress detection is important for both speech understanding and natural speech synthesis. In this paper, we report on experiments with several classifiers trained on a hand-labeled corpus, using acoustic, lexical and syntactic features. Results show that boosting neural network (NN) classifier achieves the best performance for modeling acoustic features, and that conditional random fields (CRFs) is more effective for lexical and syntactic features. The combination of the acoustic and syntactic classifiers yield 84.23％ stress detection accuracy rate. When comparing with previous work on the same training set and test set, our proposed models have better performance.

作者: Ai-Ying Zhang Hua You Chong-Jia Ni

作者单位: School of Statistics and Mathematics, Shandong University of Finance, Jinan, China School of Foreign Studies, Shandong University of Finance, Jinan, China

会议类型: 国际会议

会议名称: 第十届中国虚拟现实年会

会议地点: 上海

会议语种:英文

页码: 494-498

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）