Noise-robust voice conversion based on joint dictionary optimization  

作　　者：ZHANG Shilei JIAN Zhihua SUN Minhong ZHONG Hua LIU Erxiao 

机构地区：[1]School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018

出　　处：《Chinese Journal of Acoustics》2020年第2期259-272,共14页声学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61201301,61271214,61301248,41704154,61772166);Zhejiang Province Science and Technology Plan Project(LGG18F010009)。

摘　　要：A noise robust voice conversion algorithm based on joint dictionary optimization is proposed to effectively convert noisy source speech into the target one. In composition of the joint dictionary, speech dictionary is optimized using backward elimination algorithm. At the same time, a noise dictionary is introduced to match the noisy speech. The experimental results show that the backward elimination algorithm can reduce the number of dictionary frames and reduce the amount of calculation while ensuring the conversion effect. In low SNR and multiple noise environments, the algorithm has better conversion effect than both the traditional NMF algorithm and the NMF conversion algorithm plus spectral subtraction de-noising. The proposed algorithm improves the robustness of voice conversion system.

关 键 词：OPTIMIZATION noise BACKWARD 

分 类 号：TN912.3[电子电信—通信与信息系统]