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On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties (Contributo in atti di convegno)
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- On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties (Contributo in atti di convegno) (literal)
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- 1989-01-01T00:00:00+01:00 (literal)
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De Mori R., Bengio Y., Cosi P. (1989)
On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties
in IEEE IJCAI-89 - International Joint Conference on Artificial Intelligence, Detroit, MI, U.S.A., August 20-25, 1989
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- De Mori R., Bengio Y., Cosi P. (literal)
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- De Mori R., Bengio Y., Cosi P.
On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties
Proceedings IEEE IJCAI-89, International Joint Conference on Artificial Intelligence
Detroit, MI, U.S.A.
1989
Proceeding
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Pages 1531-1536
Morgan Kaufmann Publishers Inc. San Francisco, CA, USA ©1989
http://www.ijcai.org/Past%20Proceedings/IJCAI-89-VOL-2/PDF/108.pdf (literal)
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- ISTC CNR UOS Padova
Cosi P.
School of Computer Science Mc Gill University 3480 University str. Montreal,Que.,Canada H3A2A7
De Mori R., Bengio Y. (literal)
- Titolo
- On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties (literal)
- Abstract
- The paper describes a speech coding system
based on an ear model followed by a set of Multi-
Layer Networks (MLN). MLNs are trained to learn
how to recognize articulatory features like the
place and manner of articulation. Experiments are
performed on 10 English vowels showing a
recognition rate higher than 95% for new
speakers. When features are used for recognition,
comparable results are obtained for vowels and
diphthongs not used for training and pronounced
by new speakers. This suggests that MLNs
suitably fed by the data computed by an ear model
have good generalization capabilities over new
speakers and new sounds. (literal)
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