In this paper we propose a neural model conceived for problems of word recognition and understanding of small protocol-driven sentences. The model is based on an unified approach to integrate priori knowledge and learning by example. The priori knowledge, injected into the network connections, can be of different levels, while learning is mainly conceived as a refinement process, and is responsible of dealing with uncertainty. We describe a small prototype for problems of isolated word recognition.
Frasconi, P., Gori, M., Maggini, M., Soda, G. (1991). KL: A Neural Model for Capturing Structure in Speech. In Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence (pp.450-454). Springer Verlag [10.1007/3-540-54712-6_260].
KL: A Neural Model for Capturing Structure in Speech
Gori M.;Maggini M.;
1991-01-01
Abstract
In this paper we propose a neural model conceived for problems of word recognition and understanding of small protocol-driven sentences. The model is based on an unified approach to integrate priori knowledge and learning by example. The priori knowledge, injected into the network connections, can be of different levels, while learning is mainly conceived as a refinement process, and is responsible of dealing with uncertainty. We describe a small prototype for problems of isolated word recognition.
| File | Dimensione | Formato | |
|---|---|---|---|
|
AIIA91.pdf
non disponibili
Tipologia:
Post-print
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
304.06 kB
Formato
Adobe PDF
|
304.06 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/36657
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo