SEMI-NON-NEGATIVE MATRIX FACTORIZATION USING ALTERNATING DIRECTION METHOD OF MULTIPLIERS FOR 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. A VC method using Non-negative Matrix Factorization (NMF) has been researched because of its natural sounding voice, however, huge memory usage and high computational times have been reported as problems. We present in this paper a new VC method using Semi-Non-negative Matrix Factorization (Semi-NMF) using the Alternating Direction Method of Multipliers (ADMM) in order to tackle the problems associated with NMF-based VC. Dictionary learning using Semi-NMF can create a compact dictionary, and ADMM enables faster convergence than conventional Semi-NMF. Experimental results show that our proposed method is 76 times faster than conventional NMF, and its conversion quality is almost the same as that of the conventional method.