High quality text-to-speech (TTS) synthesis requires large amounts of computing resources (cpu and memory). To match deeply embedded applications we propose a novel approach based on soft computing methodolo-gies (fuzzy logic and neural networks). A feed-forward back propagation neural network has been trained for phonetic rules generation and a fuzzy logic engine has been tuned for prosodic control. A neural network has been used also to control the coarticulation process. A SOM neural network with fuzzy logic out-put layer has been defined for automatic utterance segmentation and labeling. Only basic phomemes units has been used for speech synthesis, demonstrating that a high-quality TTS synthesizer can be developed to target very deeply embedded systems.
NeuroFuzzy approach to the development of a text-to-speech (TTS) synthesizer for deeply embedded applications / M. Malcangi - In: Proceedings of the 14th Turkish Symposium on Artificial Intelligence and Neural NetworksIzmir : IZMIR Institute of Thechnology, 2005. - ISBN 9756590033. - pp. 179-186 (( Intervento presentato al 14. convegno Turkish Symposium on Artificial Intelligence and Neural Networks tenutosi a IZMIR nel 2005.
NeuroFuzzy approach to the development of a text-to-speech (TTS) synthesizer for deeply embedded applications
M. MalcangiPrimo
2005
Abstract
High quality text-to-speech (TTS) synthesis requires large amounts of computing resources (cpu and memory). To match deeply embedded applications we propose a novel approach based on soft computing methodolo-gies (fuzzy logic and neural networks). A feed-forward back propagation neural network has been trained for phonetic rules generation and a fuzzy logic engine has been tuned for prosodic control. A neural network has been used also to control the coarticulation process. A SOM neural network with fuzzy logic out-put layer has been defined for automatic utterance segmentation and labeling. Only basic phomemes units has been used for speech synthesis, demonstrating that a high-quality TTS synthesizer can be developed to target very deeply embedded systems.Pubblicazioni consigliate
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