State mapping based method for cross-lingual speaker adaptation in HMM-based speech synthesis

A phone mapping-based method had been introduced for cross-lingual speaker adaptation in HMM-based speech synthesis. In this paper, we continue to propose a state mapping based method for cross-lingual speaker adaptation, where the state mapping between voice models in source and target languages is established under minimum Kullback-Leibler divergence (KLD) criterion. We introduce two approaches to use the established mapping information for cross-lingual speaker adaptation, including data mapping and transform mapping approaches. From the experimental results, the state mapping based method outperformed the phone mapping based method. In addition, the data mapping approach achieved better speaker similarity, and the transform mapping approach achieved better speech quality after cross-lingual speaker adaptation.