Conversion Function Clustering and Selection for Expressive Voice Conversion

In this study, a conversion function clustering and selection approach to conversion-based expressive speech synthesis is proposed. First, a set of small-sized emotional parallel speech databases is designed and collected to train the conversion functions. Gaussian mixture bi-gram model (GMBM) is adopted as the conversion function to model the temporal and spectral evolution of speech. Conversion functions initially constructed from the parallel sub-syllable pairs in the speech database are clustered based on linguistic and spectral information. Subjective and objective evaluations with statistical hypothesis testing were conducted to evaluate the quality of the converted speech. The results show that the proposed method exhibits encouraging potential in conversion-based expressive speech synthesis.