Text-independent voice conversion based on state mapped codebook

Voice conversion has become more and more important in speech technology, but most of current works have to use parallel utterances of both source and target speaker as the training corpus, which limits the application of the technology. In the paper, we propose a new method of text-independent voice conversion which uses non-parallel corpus for the training. The Hidden Markov Model (HMM) is used to represent the phonetic structure of training speech and to generate the training pairs of source and target speakers by mapping the HMM states between source and target speeches. Then, HMM state mapped codebooks are generated to create the mapping function for the text-independent voice conversion. The subjective experiments based on ABX tests and MOS tests show that the method proposed in the paper gets the similar conversion performance and better speech quality compared to the conventional voice conversion systems.