Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling

6 Sep 2020 Songxiang Liu Yuewen Cao Disong Wang Xixin Wu Xunying Liu Helen Meng

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq) based, non-parallel voice conversion approach. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq based synthesis module... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper