R
R. De Mori
Researcher at University of Avignon
Publications - 61
Citations - 2811
R. De Mori is an academic researcher from University of Avignon. The author has contributed to research in topics: Language model & Hidden Markov model. The author has an hindex of 19, co-authored 61 publications receiving 2671 citations. Previous affiliations of R. De Mori include Carnegie Mellon University & McGill University.
Papers
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Journal ArticleDOI
A cache-based natural language model for speech recognition
Roland Kuhn,R. De Mori +1 more
TL;DR: A novel kind of language model which reflects short-term patterns of word use by means of a cache component (analogous to cache memory in hardware terminology) is presented and contains a 3g-gram component of the traditional type.
Journal ArticleDOI
Automatic speech recognition and speech variability: A review
Mohamed Faouzi BenZeghiba,R. De Mori,Olivier Deroo,Stéphane Dupont,T. Erbes,D. Jouvet,Luciano Fissore,Pietro Laface,Alfred Mertins,Christophe Ris,Richard Rose,Vivek Tyagi,Christian Wellekens +12 more
TL;DR: Current advances related to automatic speech recognition (ASR) and spoken language systems and deficiencies in dealing with variation naturally present in speech are outlined.
Journal ArticleDOI
Global optimization of a neural network-hidden Markov model hybrid
TL;DR: In the approach described, the ANN outputs constitute the sequence of observation vectors for the HMM, and an algorithm is proposed for global optimization of all the parameters.
Journal ArticleDOI
Spoken language understanding
TL;DR: Spoken language understanding and natural language understanding share the goal of obtaining a conceptual representation of natural language sentences and computational semantics performs a conceptualization of the world using computational processes for composing a meaning representation structure from available signs.
Journal ArticleDOI
The application of semantic classification trees to natural language understanding
Roland Kuhn,R. De Mori +1 more
TL;DR: A new data structure is described, the semantic classification tree (SCT), that learns semantic rules from training data and can be a building block for robust matchers for NLU tasks.