Individuality-preserving Voice Conversion for Articulation Disorders Using Dictionary Selective Non-negative Matrix Factorization
Ryo Aihara, Tetsuya Takiguchi, Yasuo Ariki · 2014
We present in this paper a voice conversion (VC) method for a person with an articulation disorder resulting from athetoid cerebral palsy.The movements of such speakers are 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.In order to preserve the speaker's individuality, we use a combined dictionary that was constructed from the source speaker's vowels and target speaker's consonants.However, this exemplar-based approach needs to hold all the training exemplars (frames), and it may cause mismatching of phonemes between input signals and selected exemplars.In this paper, in order to reduce the mismatching of phoneme alignment, we propose a phoneme-categorized sub-dictionary and a dictionary selection method using NMF.The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based and conventional NMFbased method.