Entropy-based sentence selection for speech synthesis using phonetic and prosodic contexts

This paper proposes a sentence selection method using a maxi- mum entropy criterion to construct recording scripts for speech synthesis. In the conventional corpus design of speech syn- thesis, a greedy algorithm that maximizes phonetic coverage is often used. However, for statistical parametric speech syn- thesis, phonetic and prosodic contextual balance is important as well as the coverage. To take account of both of the pho- netic and prosodic contextual balance in the sentence selection, we introduce and maximize the entropy of the phonetic and prosodic contexts, such as biphone, triphone, accent, and sen- tence length. The objective experimental results show that the proposed method achieves better coverage and balance of con- texts and reduces spectral and F0 distortions compared to the random and coverage-based sentence selection methods.