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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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Abstract:
International audience ; Recent neurocognitive models commonly consider speech perception as a hierarchy of processes, each corresponding to specific temporal scales of collective oscillatory processes in the cortex: 30–80 Hz gamma oscillations in charge of phonetic analysis, 4–9 Hz theta oscillations in charge of syllabic segmentation, 1–2 Hz delta oscillations processing prosodic/syntactic units and the 15–20 Hz beta channel possibly involved in top-down predictions. Several recent neuro-computational models thus feature theta oscillations, driven by the speech acoustic envelope, to achieve syllabic parsing before lexical access. However, it is unlikely that such syllabic parsing, performed in a purely bottom-up manner from envelope variations, would be totally efficient in all situations, especially in adverse sensory conditions. We present a new probabilistic model of spoken word recognition, called COSMO-Onset, in which syllabic parsing relies on fusion between top-down, lexical prediction of onset events and bottom-up onset detection from the acoustic envelope. We report preliminary simulations, analyzing how the model performs syllabic parsing and phone, syllable and word recognition. We show that, while purely bottom-up onset detection is sufficient for word recognition in nominal conditions, top-down prediction of syllabic onset events allows overcoming challenging adverse conditions, such as when the acoustic envelope is degraded, leading either to spurious or missing onset events in the sensory signal. This provides a proposal for a possible computational functional role of top-down, predictive processes during speech recognition, consistent with recent models of neuronal oscillatory processes.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SCCO.LING]Cognitive science/Linguistics; [SCCO.NEUR]Cognitive science/Neuroscience; Bayesian modeling; bottom-up event detection; neural oscillations; speech perception; spoken word recognition; syllabic parsing; top-down prediction
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URL: https://doi.org/10.3389/fnsys.2021.653975
https://hal.archives-ouvertes.fr/hal-03318691
https://hal.archives-ouvertes.fr/hal-03318691/file/nabe%CC%8121.pdf
https://hal.archives-ouvertes.fr/hal-03318691/document
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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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| 18 |
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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