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Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation ...
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COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets
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In: ISSN: 1662-5137 ; Frontiers in Systems Neuroscience ; https://hal.archives-ouvertes.fr/hal-03318691 ; Frontiers in Systems Neuroscience, Frontiers, 2021, 15, pp.653975. ⟨10.3389/fnsys.2021.653975⟩ (2021)
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COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets
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In: Front Syst Neurosci (2021)
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Simulating length and frequency effects across multiple tasks with the Bayesian model BRAID-Phon
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In: 42nd Annual Virtual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-02913396 ; 42nd Annual Virtual Meeting of the Cognitive Science Society, Jul 2020, Toronto, Canada. pp.3158-3163 ; https://cognitivesciencesociety.org/cogsci-2020/ (2020)
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Towards an articulatory-driven neural vocoder for speech synthesis
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In: ISSP 2020 - 12th International Seminar on Speech Production ; https://hal.archives-ouvertes.fr/hal-03184762 ; ISSP 2020 - 12th International Seminar on Speech Production, Dec 2020, Providence (virtual), United States (2020)
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Speakers are able to categorize vowels based on tongue somatosensation
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In: Proc Natl Acad Sci U S A (2020)
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Modeling the length effect for words in lexical decision: The role of visual attention
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In: ISSN: 0042-6989 ; EISSN: 0042-6989 ; Vision Research ; https://hal.archives-ouvertes.fr/hal-02097508 ; Vision Research, Elsevier, 2019 (2019)
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Computer simulations of coupled idiosyncrasies in speech perception and speech production with COSMO, a perceptuo-motor Bayesian model of speech communication
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In: ISSN: 1932-6203 ; EISSN: 1932-6203 ; PLoS ONE ; https://hal.sorbonne-universite.fr/hal-01994708 ; PLoS ONE, Public Library of Science, 2019, 14 (1), pp.e0210302. ⟨10.1371/journal.pone.0210302⟩ (2019)
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Modeling Word Length Effect in Lexical Decision: The Role of Visual Attention
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In: Annual Meeting of the Psychonomic Society ; https://hal.archives-ouvertes.fr/hal-02004148 ; Annual Meeting of the Psychonomic Society, Nov 2018, New-Orleans, United States. pp.80-81, 2018 (2018)
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Bayesian modeling of lexical knowledge acquisition in BRAID, a visual word recognition model
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In: Conference of the Society of Scientific Studies of Reading (SSSR) ; https://hal.archives-ouvertes.fr/hal-02004280 ; Conference of the Society of Scientific Studies of Reading (SSSR), Jul 2018, Brighton, United Kingdom (2018)
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Reconciling opposite neighborhood frequency effects in lexical decision: Evidence from a novel probabilistic model of visual word recognition
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In: Conference of the Society of Scientific Studies of Reading (SSSR) ; https://hal.archives-ouvertes.fr/hal-02004264 ; Conference of the Society of Scientific Studies of Reading (SSSR), Jul 2018, Brighton, United Kingdom (2018)
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Reconciling opposite neighborhood frequency effects in lexical decision: Evidence from a novel probabilistic model of visual word recognition
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In: Proceedings of the 40th Annual Conference of the Cognitive Science Society ; 40th Annual Conference of the Cognitive Science Society (CogSci 2018) ; https://hal.archives-ouvertes.fr/hal-01850020 ; 40th Annual Conference of the Cognitive Science Society (CogSci 2018), Jul 2018, Madison, WI, United States. pp.2238-2243 ; http://www.cognitivesciencesociety.org/conference/cogsci-2018/ (2018)
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COSMO SylPhon: A Bayesian Perceptuo-motor Model to Assess Phonological Learning
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In: Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02002373 ; Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association, Sep 2018, Hyderabad, India. pp.3786-3790, ⟨10.21437/interspeech.2018-73⟩ (2018)
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Reanalyzing neurocognitive data on the role of the motor system in speech perception within COSMO, a Bayesian perceptuo-motor model of speech communication
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Enhancing reading performance through action video games: the role of visual attention span
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In: ISSN: 2045-2322 ; EISSN: 2045-2322 ; Scientific Reports ; https://hal.archives-ouvertes.fr/hal-01654841 ; Scientific Reports, Nature Publishing Group, 2017, 7 (1), pp.14563. ⟨10.1038/s41598-017-15119-9⟩ (2017)
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Les modèles computationnels de lecture
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In: Traité de neurolinguistique ; https://hal.archives-ouvertes.fr/hal-01420329 ; Traité de neurolinguistique, pp.167-182, 2016 (2016)
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COSMO (“Communicating about Objects using Sensory–Motor Operations”): A Bayesian modeling framework for studying speech communication and the emergence of phonological systems
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In: ISSN: 0095-4470 ; EISSN: 1095-8576 ; Journal of Phonetics ; https://hal.archives-ouvertes.fr/hal-01230175 ; Journal of Phonetics, Elsevier, 2015, 53, pp.5-41. ⟨10.1016/j.wocn.2015.06.001⟩ ; http://www.sciencedirect.com/science/article/pii/S0095447015000352 (2015)
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COSMO, a Bayesian computational model of speech communication: Assessing the role of sensory vs. motor knowledge in speech perception
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In: 5th International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob) ; https://hal.archives-ouvertes.fr/hal-02004350 ; 5th International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob), Aug 2015, Providence, RI, United States (2015)
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Modeling concurrent development of speech perception and production in a Bayesian framework
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In: WILD 2015 - 2nd Workshop on Infant Language Development ; https://hal.archives-ouvertes.fr/hal-01202417 ; WILD 2015 - 2nd Workshop on Infant Language Development, Jun 2015, Stockholm, Sweden (2015)
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Bayesian Algorithmic Modeling in Cognitive Science ; Modélisation bayésienne algorithmique en science cognitive
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In: https://hal.archives-ouvertes.fr/tel-01237127 ; Computer science. Université Grenoble Alpes, 2015 (2015)
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Abstract:
In the domain of modeling sensorimotor systems, whether they are artificial or natural, we are interested in defining and studying structured probabilistic models of cognitive functions and cognitive representations. To do so, we use the Bayesian Programming framework, originally developed in the domain of robotic programming. It provides a mathematically unified language to express and manipulate knowledge, in arbitrarily complex models.We apply it to cognitive modeling, obtaining Bayesian Algorithmic Models of several perception and action systems. We thus define the BAP model for isolated cursive letter reading and writing, the BRAID model for word recognition, the COSMO-Emergence model for communication code emergence, the COSMO-Perception model for syllable perception, and the COSMO-Production model for phoneme sequence production.We then discuss the place of Bayesian Algorithmic Modeling in the current panorama of Bayesian modeling in Cognitive Science, arguing for the need for a clear distinction between computational and algorithmic accounts of cognitive functions, and advocating a model comparison methodology for exploring and constraining the properties of complex probabilistic models in a formally principled manner. ; Dans le domaine de la modélisation des systèmes sensorimoteurs, qu’ils soient artificiels ou naturels, nous nous intéressons à la définition et à l’étude de modèles probabilistes structurés des fonctions et représentations cognitives. Dans ce but, nous utilisons le formalisme de la Programmation Bayésienne, développé initialement dans le domaine de la programmation robotique. Il offre un langage mathématiquement unifié pour exprimer et manipuler des connaissances, dans des modèles arbitrairement complexes.Nous l’appliquons à la modélisation cognitive, pour obtenir des Modèles Bayésiens Algorithmiques de plusieurs systèmes perceptifs et moteurs. De cette manière, nous définissons le modèle BAP pour la lecture et l’écriture de lettres cursives isolées, le modèle BRAID pour la reconnaissance de mots, le modèle COSMO-Emergence pour l’émergence de codes de communication, le modèle COSMO-Perception pour la perception des syllabes, le modèle COSMO-Production pour la production de séquences de phonèmes.Nous discutons enfin la place de la Modélisation Bayésienne Algorithmique dans le panorama actuel de la modélisation bayésienne en Science Cognitive, défendant le besoin d’une distinction claire entre les explications computationnelles et algorithmiques des fonctions cognitives, et proposant une méthodologie de comparaison de modèles pour explorer et contraindre les propriétés de modèles probabilistes complexes, d’une manière systématique.
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Keyword:
[SCCO.COMP]Cognitive science/Computer science; Bayesian modeling; Bayesian Programming; Cognitive Science; lecture et écriture; Modélisation bayésienne; perception et production de la parole; Programmation bayésienne; reading and writing; Science Cognitive; speech perception and production
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URL: https://hal.archives-ouvertes.fr/tel-01237127/document https://hal.archives-ouvertes.fr/tel-01237127 https://hal.archives-ouvertes.fr/tel-01237127/file/HDR_Diard.pdf
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