Speaker and language adaptive training for HMM-based polyglot speech synthesis

This paper proposes a novel technique for speaker and language adaptive training for HMM-based statistical parametric polyglot speech synthesis. Language-specific context-dependencies in the system are captured using CAT with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by CMLLR-based transforms. This framework allows multi-speaker/multi-language adaptive training and synthesis to be performed. Experimental results show that the proposed technique achieves better naturalness than both speaker-adaptively trained language-dependent and language-independent models.