Noise-robust voice conversion using a small parallel data based on non-negative matrix factorization

This paper presents a novel framework of voice conversion (VC) based on non-negative matrix factorization (NMF) using a small parallel corpus. In our previous work, a VC technique using NMF for noisy environments has been proposed, and it requires parallel exemplars (dictionary), which con sist of the source exemplars and target exemplars, having the same texts uttered by the source and target speakers. The large parallel corpus is used to construct a conversion function in NMF-based VC (in the same way as common GMM-based VC). In this paper, an adaptation matrix in an NMF frame work is introduced to adapt the source dictionary to the target dictionary. This adaptation matrix is estimated using a small parallel speech corpus only. The effectiveness of this method is confirmed by comparing its effectiveness with that of a con ventional NMF-based method and a GMM-based method in a noisy environment.

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