Individuality-preserving voice conversion for articulation disorders based on non-negative matrix factorization

We present in this paper a voice conversion (VC) method for a person with an articulation disorder resulting from athetoid cerebral palsy. The movement of such speakers is limited by their athetoid symptoms, and their consonants are often unstable or unclear, which makes it difficult for them to communicate. In this paper, exemplar-based spectral conversion using Non-negative Matrix Factorization (NMF) is applied to a voice with an articulation disorder. To preserve the speaker´s individuality, we used a combined dictionary that is constructed from the source speaker´s vowels and target speaker´s consonants. Experimental results indicate that the performance of NMF-based VC is considerably better than conventional GMM-based VC.