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.
This paper describes a novel method for text-independent voice conversion using improved state mapping. HMM is used for representing the phonetic structure of training speech. Centroids of the common phonemes between source and target speech are utilized as phonetic anchors while establishing a mapping between acoustic spaces of source and target speakers. These phonetic anchors and weighted linear transform are used for creating a continuous parametric mapping from source to target speech parameters. The proposed technique is applicable to both intra-lingual and cross-lingual voice conversion. Experimental results show that state mapping is improved using proposed technique.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
