Title: Noise-robust voice conversion based on joint dictionary optimization

Author(s): ZHANG Shilei; JIAN Zhihua; SUN Minhong; ZHONG Hua; LIU Erxiao;

Affiliation(s): School of Communication Engineering, Hangzhou Dianzi University

Abstract: 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.