期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Voice conversion using structured Gaussian mixture model in cepstrum eigenspace 被引量:2
1
作者 LI Yangchun YU Yibiao 《Chinese Journal of Acoustics》 CSCD 2015年第3期325-336,共12页
A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech ar... A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech are extracted, and mapped to the eigenspace which is formed by eigenvectors of its scatter matrix, thereby the Structured Gaussian Mixture Model in the EigenSpace (SGMM-ES) is trained. The source and target speaker's SGMM-ES are matched based on Acoustic Universal Structure (AUS) principle to achieve spectrum transform function. Experimental results show the speaker identification rate of conversion speech achieves 95.25%, and the value of average cepstrum distortion is 1.25 which is 0.8% and 7.3% higher than the performance of SGMM method respectively. ABX and MOS evaluations indicate the conversion performance is quite close to the traditional method under the parallel corpora condition. The results show the eigenspace based structured Gaussian mixture model for voice conversion under the non-parallel corpora is effective. 展开更多
关键词 LPCC Voice conversion using structured Gaussian mixture model in cepstrum eigenspace ES GMM
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部