Abstract
In concatenative synthesis, speech is produced by joining segments automatically selected among units contained in a previously segmented database. The synthetic speech resulting from such a technique is often improved when accurate segmentation tools are considered. The performance of these tools is often enhanced by a hybrid approach resulting from the association of an HMM modeling with a boundary refining process. Such a refining has been carried out sucessfully by using techniques based on neural networks. This paper presents a set of networks that outperform other topologies discussed in the literature. These networks are trained by performing a clusterization of the training set taking into consideration phonetic transitions with similarities to each other.
This work was partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq), Studies and Projects Funding Body (FINEP), and Dígitro Tecnologia Ltda.
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Nicodem, M.V., Kafka, S.G., Seara, R., Seara, R. (2008). Boundary Refining Aiming at Speech Synthesis Applications. In: Teixeira, A., de Lima, V.L.S., de Oliveira, L.C., Quaresma, P. (eds) Computational Processing of the Portuguese Language. PROPOR 2008. Lecture Notes in Computer Science(), vol 5190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85980-2_8
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DOI: https://doi.org/10.1007/978-3-540-85980-2_8
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