Prosodic modeling with rich syntactic context in HMM-based Mandarin speech synthesis

To further explore the relevance between prosody and syntax information, we propose a novel approach of prosodic modeling with rich syntactic context instead of prosodic structure in HMM-based Mandarin speech synthesis. Considering the characteristics of Mandarin itself, word-based and character-based syntactic parsings are investigated in this study respectively. This method can not only avoid the existing cascade error in conventional way of prosodic parameter prediction but also not rely on the manually annotated corpora of prosodic structure. Experimental results show that even though automatic syntactic parsing has limited precision, prosodic modeling with rich syntactic context could still achieve significant better performance than the way of the manually annotated prosodic corpora, especially in duration evaluation.