Abstract
We present ongoing work on prosody prediction for speech synthesis. This approach considers sentences as tree-like structures and decides on the prosody from a corpus of such structures using machine learning techniques. The prediction is achieved from the prosody of the closest sentence of the corpus through tree similarity measurements in a nearest neighbour context. We introduce a syntactic structure and a performance structure representation, the tree similarity metrics considered, and then we discuss the prediction method. Experiments are currently under process to qualify this approach.
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Blin, L., Edgington, M. (2000). Prosody Prediction from Tree-Like Structure Similarities. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2000. Lecture Notes in Computer Science(), vol 1902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45323-7_62
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DOI: https://doi.org/10.1007/3-540-45323-7_62
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