Title: An algorithm for voice conversion with limited corpus
Author(s): GU Dong; JIAN Zhihua;
Affiliation(s): School of Communication Engineering, Hangzhou Dianzi University
Abstract: Under the condition of limited target speaker's corpus, this paper proposed an algorithm for voice conversion using unified tensor dictionary with limited corpus. Firstly, parallel speech of N speakers was selected randomly from the speech corpus to build the base of tensor dictionary. And then, after the operation of multi-series dynamic time warping for those chosen speech, N two-dimension basic dictionaries can be generated which constituted the unified tensor dictionary. During the conversion stage, the two dictionaries of source and target speaker were established by linear combination of the N basic dictionaries using the two speakers' speech. The experimental results showed that when the number of the basic speaker was 14, our algorithm can obtain the compared performance of the traditional NMF-based method with few target speaker corpus, which greatly facilitate the application of voice conversion system.