Abstract
In this paper we investigate the relationship between various lexical and prosodic features and semantic abnormality, the occurrence of unusual or unexpected events, in generating speech for MAGIC, which employs a Concept-to-Speech system to generate post-operative reports for patients who have undergone bypass surgery. Using the speech corpus collected for this application, we conducted empirical analysis to systematically discover significantly correlated prosodic and lexical features. The automatically learned abnormality model not only can be used in building comprehensive prosody prediction systems for Concept-to-Speech generation, but also help identify unusual information during speech analysis and understanding.
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