Activity-mapping non-negative matrix factorization for exemplar-based voice conversion

Voice conversion (VC) is being widely researched in the field of speech processing because of increased interest in using such processing in applications such as personalized Text-To-Speech systems. We present in this paper an exemplar-based VC method using Non-negative Matrix Factorization (NMF), which is different from conventional statistical VC. In our previous exemplar-based VC method, input speech is represented by the source dictionary and its sparse coefficients. The source and the target dictionaries are fully coupled and the converted voice is constructed from the source coefficients and the target dictionary. In this paper, we propose an Activity-mapping NMF approach and introduce mapping matrices between source and target sparse coefficients. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method and a conventional NMF-based method.