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Unité de recherche
PCRD EU
Numéro de projet
98.0086
Titre du projet
RESPITE: Recognition of speech by partial information techniques
Titre du projet anglais
RESPITE: Recognition of speech by partial information techniques

Textes relatifs à ce projet

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Mots-clé
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Description succincte
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Partenaires et organisations internationales
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Résumé des résultats (Abstract)
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Textes saisis


CatégorieTexte
Mots-clé
(Anglais)
Speech recognition; hidden markov models; missing data theory; subband based speech recognition
Autre Numéro de projet
(Anglais)
EU project number: 28149
Programme de recherche
(Anglais)
EU-programme: 4. Frame Research Programme - 1.3 Telematic systems
Description succincte
(Anglais)
See abstract
Autres indications
(Anglais)
Full name of research-institution/enterprise:
Institut dalle Molle d'intelligence artificielle perceptive IDIAP
Research Institute
Partenaires et organisations internationales
(Anglais)
Sheffield University (UK), Daimler-Chrysler (D), Faculté Polytechnique de Mons (B), ICP (F), Babel technology (B)
Résumé des résultats (Abstract)
(Anglais)
The RESPITE project aimed at developing techniques for automatic speech recognition that are truly robust to unanticipated noise and corruption, with possible car applications (with Daimler-Chrysler as our main industrial partner). The techniques developed and tested in several conditions were based on a combination of emergent theories of decision-making from multiple, incomplete evidence sources and of human speech perception. More specifically, new recognition paradigms based on multi-stream processing and the missing data theory have been investigated in much detail and tested on different databases (including in the framework of international evaluation campaigns), on real data (recorded in car environments), as well as part of a fully integrated in-car system.
At the end of the RESPITE project, the main conclusions are that the resulting system based on missing data and multi-stream speech recognition techniques are more robust to noise, without requiring specific noise adaptation or noise model. Being adopted by numerous labs, it is also expected that these new techniques could yield advances in adjacent recent fields, such as the handling of multiple temporal resolutions and the processing of multi-modal information (e.g., audio-visual fusion
Références bases de données
(Anglais)
Swiss Database: Euro-DB of the
State Secretariat for Education and Research
Hallwylstrasse 4
CH-3003 Berne, Switzerland
Tel. +41 31 322 74 82
Swiss Project-Number: 98.0086