CLASSiC
Computational Learning in Adaptive Systems for Spoken Conversation
| Coordinatore | HERIOT-WATT UNIVERSITY
Organization address
address: Riccarton, Scott Russell Building contact info |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 4˙767˙314 € |
| EC contributo | 3˙400˙000 € |
| Programma | FP7-ICT
Specific Programme "Cooperation": Information and communication technologies |
| Code Call | FP7-ICT-2007-1 |
| Funding Scheme | CP |
| Anno di inizio | 2008 |
| Periodo (anno-mese-giorno) | 2008-03-01 - 2011-02-28 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
HERIOT-WATT UNIVERSITY
Organization address
address: Riccarton, Scott Russell Building contact info |
UK (Edinburgh) | coordinator | 0.00 |
| 2 |
ECOLE SUPERIEURE D'ELECTRICITE
Organization address
address: RUE JOLIOT CURIE - PLATEAU DE MOULON contact info |
FR (GIF SUR YVETTE CEDEX) | participant | 0.00 |
| 3 |
FRANCE TELECOM SA
Organization address
address: PLACE D ALLERAY contact info |
FR (PARIS) | participant | 0.00 |
| 4 |
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Organization address
address: The Old Schools, Trinity Lane contact info |
UK (CAMBRIDGE) | participant | 0.00 |
| 5 |
THE UNIVERSITY OF EDINBURGH
Organization address
address: OLD COLLEGE, SOUTH BRIDGE contact info |
UK (EDINBURGH) | participant | 0.00 |
| 6 |
UNIVERSITE DE GENEVE
Organization address
address: Rue du General Dufour contact info |
CH (GENEVE 4) | participant | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
Significant advances in human-computer interaction will require systems which can exhibit truly cognitive behaviour. This is particularly so in spoken dialogue systems (SDS) where, despite wide deployment and significant investment, current systems are still limited in capability and fragile to changes in environment or application. nnRecent advances in statistical modelling and machine learning offer the potential for making a significant step forward in SDS. By both exploiting and extending these advances, the CLASSiC project will improve generalization to unexpected situations. By modelling the whole end-to-end system as an integrated statistical process, the CLASSiC project will demonstrate a qualitative leap in the adaptivity, flexibility, robustness, and naturalness of SDS.nnThe CLASSiC partners will develop a modular processing framework with an explicit representation of uncertainty which connects the various sources of uncertainty (understanding errors, ambiguity, etc) to the constraints to be exploited (task, dialogue, and user contexts). This architecture will support a layered hierarchy of supervised learning and reinforcement learning in order to facilitate mathematically principled optimisation and adaptation techniques. The architecture will be developed in close cooperation with our industrial partner in order to ensure that it provides a practical deployment platform as well as a flexible research test-bed.nnThe resulting CLASSiC SDS will be able to adapt autonomously both to the needs of different users and to changing operating environments, and to learn through experience. The data-driven methodology will also enable faster and lower-cost system implementation through automatic optimisation. Overall, the project will demonstrate not only a step-change in the capability of practical spoken dialogue systems, it will also mark a significant step forward in the longer term goal of endowing autonomous systems with truly human-like capabilities.