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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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Abstract:
International audience ; It is widely accepted that motor and auditory processes interact in speech perception, but little is known about the functional role motor processes play in the development of speech perception. To address this question we consider a Bayesian model of speech perception development based on three sets of variables: motor representations M, sensory representations S and objects O (e.g. phonological units such as phonemes). The model comprises two internal branches. Firstly, an auditory identification sub-system connects S and O. Secondly, a motor sub-system connecting M and O and a sensori-motor model connecting M and S can be combined to provide “motor identification” of sounds S, from S to M and from M to O, in an analysis-by-synthesis process. Development is modeled as a learning process in which a master iteratively produces a sensory percept S associated with an object O. The learning agent updates its auditory sub-system by observing S and O. Update of the two other branches is more complex and based on an imitation phase. The learning agent estimates a likely motor action M from input S, produces this M and observes the resulting sound S’. M, S’ and O are used to update both the motor sub-system (M, O) and the sensori-motor model (S, M). We show that the auditory identification sub-system learns rapidly, and becomes efficient for stimuli close to those provided by the master, although it generalizes poorly. By contrast, the two other sub-systems evolve more slowly, and in consequence the motor identification system performs less accurately. However, motor identification happens to have captured more variable situations during learning, and generalizes better (e.g. in noise). This is in line with a developmental schedule in which auditory processing is mature before motor knowledge (Kuhl et al, 2008) and is exploited by infants after 11 months of age for analysis-by-synthesis of unusual speech stimuli (Kuhl et al., 2014).
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Keyword:
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [SCCO.COMP]Cognitive science/Computer science
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URL: https://hal.archives-ouvertes.fr/hal-01202417
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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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