Artificial intelligence in speech understanding: Two applications at C.R.I.N.
Identifieur interne : 002A16 ( Istex/Corpus ); précédent : 002A15; suivant : 002A17Artificial intelligence in speech understanding: Two applications at C.R.I.N.
Auteurs : N. Carbonell ; J. P. Haton ; J. M. PierrelSource :
- Computers and the Humanities [ 0010-4817 ] ; 1986-07-01.
English descriptors
- Teeft :
- Artificial intelligence, Automatic speech recognition, Brief survey, Building expert systems, Carbonell, Continuous speech recognition, Control structures, Decoding, Discours continu, Expert systems, Expertise, Haton, Human expert, Knowledge base, Knowledge sources, Linguistic constraints, Mental activity, Natural language, Phonetic, Phonetic decoding, Phonological interpretation, Pierrel, Potential applications, Production rules, Production systems, Same type, Spectrogram, Spectrogram reading, Speech communication, Speech recognition, Speech understanding, Static data, Such knowledge, Voice dialogues.
Abstract
Summary: For the past fifteen years the “Pattern Recognition and Artificial Intelligence” research group at thecrin has been involved in various projects on isolated-word and continuous speech recognition. It is now clear that the recognition of natural speech necessitates making optimum use of all available knowledge sources. Present artificial intelligence techniques, especially knowledge-based systems, can be very helpful within this framework. In this paper we explain how in our projects these techniques are taken into account: expert systems for acoustic-phonetic decoding and phonological interpretation, multi-knowledge sources cooperation for actual man-machine dialogue implementation. The basic ideas are illustrated with short examples.
Url:
DOI: 10.1007/BF02404456
Links to Exploration step
ISTEX:B2042F21328D50C9EE7438B493DE1E86C0DFC5BCLe document en format XML
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It is now clear that the recognition of natural speech necessitates making optimum use of all available knowledge sources. Present artificial intelligence techniques, especially knowledge-based systems, can be very helpful within this framework. In this paper we explain how in our projects these techniques are taken into account: expert systems for acoustic-phonetic decoding and phonological interpretation, multi-knowledge sources cooperation for actual man-machine dialogue implementation. 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