Automatic voice quality measurement based on efficient combination of multiple features

摘要：

This work proposes higher-order statistics (HOS)-based features to improve classification performance of voice quality measurement. They are means and variances of skewness and kurtosis which show meaningful differences in normal, breathy, and rough voices. Jitter, shimmer, and harmonic to noise ratio (HNR) are implemented as conventional features. The performances are measured by classification and regression tree (CART) analysis. Specifically, the CART-based method by utilizing both conventional and HOS-based features is shown to be an effective for voice quality measurement, with an 89.7％ classification rate.

作者: Ji-Yeoun Lee Sangbae Jeong Minsoo Hahn Hong-Shik Choi

作者单位: Information and Communications University 119, Munjiro, Yuseong-gu, Daejeon 305-732, Korea Department of Otorhinolaryngology Yongdong Severance Hospital Yonsei University College of Medicine,

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 1272-1275

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）