Phoneme cluster based state mapping for text-independent voice conversion

This paper takes phonetic information into account for data alignment in text-independent voice conversion. Hidden Markov models are used for representing the phonetic structure of training speech. States belonging to same phoneme are grouped together to form a phoneme cluster. A state mapped codebook based transformation is established using information on the corresponding phoneme clusters from source and targets speech and weighted linear transform. For each source vector, several nearest clusters are considered simultaneously while mapping in order to generate a continuous and stable transform. Experimental results indicate that the proposed use of phonetic information increases the similarity between converted speech and target speech. The proposed technique is applicable to both intra-lingual and cross-lingual voice conversion.