Automatic Lexical Stress Detection Using Acoustic Features for Computer-Assisted Language Learning

摘要：

his paper proposes an English lexical stress detection approach using acoustic features. The approach classifies the vowels of English words into two patterns: primary stress and unstress. We firstly choose the frame-averaged basic feature set of the individual syllable nucleus in polysyllabic words as the baseline to decide the stress pattern. This feature set includes the semitone, the duration, the loudness and the emphasis feature. Furthermore, we introduce the pitch-variation feature set and the context-aware feature set to describe the inside variation characteristic and outside contextual characteristic of the syllable nucleus. By combining the three feature sets, the accuracy rate is improved by 7％

作者: Junhong Zhao Hua Yuan Jia Liu ShanHong Xia

作者单位: Key Laboratory on Transducing Technology, Institute of Electronics,Chinese Academy of Sciences, Beij Tsinghua National Laboratory for Information Science and TechnologyDepartment of Electronic Engineer Tsinghua National Laboratory for Information Science and Technology Department of Electronic Enginee

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-5

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）