ROBUST GMM BASED GENDER CLASSIFICATION USING PITCH AND RASTA-PLP PARAMETERS OF SPEECH

摘要：

A novel gender classification system has been proposed based on Gaussian Mixture Models, which apply the combined parameters of pitch and 10th order relative spectral perceptual linear predictive coefficients to model the characteristics of male and female speech. The performances of gender classification system have been evaluated on the conditions of clean speech, noisy speech and multi-language. The simulations show that the performance of the proposed gender classifier is excellent; it is very robust for noise and completely independent of languages; the classification accuracy is as high as above 98％ for all clean speech and remains 95％ for most noisy speech, even the SNR of speech is degraded to 0dB.

关键词： Gender classification GMM RASTA-PLP

作者: YU-MIN ZENG ZHEN-YANG WU TIAGO FALK WAI-YIP CHAN

作者单位: Department of Radio Engineering, Southeast University, Nanjing 210096, China;School of Physics Scien Department of Radio Engineering, Southeast University, Nanjing 210096, China Department of Electrical and Computer Engineering, Queens University, Kingston, K7L 3N6, Canada

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3376-3379

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）