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The Zero Resource Speech Challenge 2021: Spoken language modelling
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In: ISSN: 0162-8828 ; IEEE Transactions on Pattern Analysis and Machine Intelligence ; https://hal.inria.fr/hal-03329301 ; IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. ⟨10.1109/TPAMI.2021.3083839⟩ (2021)
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The Zero Resource Speech Challenge 2021: Spoken language modelling
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In: Interspeech 2021 - Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-03329301 ; Interspeech 2021 - Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. ⟨10.1109/TPAMI.2021.3083839⟩ (2021)
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The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
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In: NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing ; https://hal.archives-ouvertes.fr/hal-03070362 ; NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing, Dec 2020, Virtuel, France (2020)
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The Perceptimatic English Benchmark for Speech Perception Models
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In: CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03087248 ; CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society, Jul 2020, Toronto / Virtual, Canada (2020)
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Perceptimatic: A human speech perception benchmark for unsupervised subword modelling
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In: Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03087252 ; Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China ; http://www.interspeech2020.org/ (2020)
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Modelling Perceptual Effects of Phonology with ASR Systems
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In: CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03070281 ; CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society, Jul 2020, Virtual, France (2020)
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The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
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In: Interspeech 2020 - Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02962224 ; Interspeech 2020 - Conference of the International Speech Communication Association, Oct 2020, Shangai / Virtual, China (2020)
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Analogies minus analogy test: measuring regularities in word embeddings
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In: CoNLL 2020 - 24th Conference on Computational Natural Language Learning ; https://hal.archives-ouvertes.fr/hal-03070260 ; CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Nov 2020, Virtual, France (2020)
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Independent and Automatic Evaluation of Speaker-Independent Acoustic-to-Articulatory Reconstruction
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In: Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03087264 ; Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China ; http://www.interspeech2020.org/ (2020)
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Analogies minus analogy test: measuring regularities in word embeddings ...
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Perceptimatic: A human speech perception benchmark for unsupervised subword modelling ...
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Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Comparing unsupervised speech learning directly to human performance in speech perception
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In: Proceedings of the Annual Conference of the Cognitive Science Society (Cog Sci) ; CogSci 2019 - 41st Annual Meeting of Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-02274499 ; CogSci 2019 - 41st Annual Meeting of Cognitive Science Society, Jul 2019, Montréal, Canada (2019)
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Generative grammar, neural networks, and the implementational mapping problem: Response to Pater
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In: ISSN: 0097-8507 ; EISSN: 1535-0665 ; Language ; https://hal.archives-ouvertes.fr/hal-02274522 ; Language, Linguistic Society of America, 2019, 95 (1), pp.e87-e98. ⟨10.1353/lan.2019.0013⟩ (2019)
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RNNs Implicitly Implement Tensor Product Representations
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In: International Conference on Learning Representations ; ICLR 2019 - International Conference on Learning Representations ; https://hal.archives-ouvertes.fr/hal-02274498 ; ICLR 2019 - International Conference on Learning Representations, May 2019, New Orleans, United States (2019)
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The Zero Resource Speech Challenge 2019: TTS without T
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In: Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02274112 ; Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019)
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Mouse tracking as a window into decision making
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-02274523 ; Behavior Research Methods, Psychonomic Society, Inc, 2019, 51 (3), pp.1085-1101. ⟨10.3758/s13428-018-01194-x⟩ (2019)
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Abstract:
International audience ; Mouse tracking promises to be an efficient method to investigate the dynamics of cognitive processes: It is easier to deploy than eyetracking, yet in principle it is much more fine-grained than looking at response times. We investigated these claimed benefits directly, asking how the features of decision processes—notably, decision changes—might be captured in mouse movements. We ran two experiments, one in which we explicitly manipulated whether our stimuli triggered a flip in decision, and one in which we replicated more ecological, classical mouse-tracking results on linguistic negation (Dale & Duran, Cognitive Science, 35, 983–996, 2011). We concluded, first, that spatial information (mouse path) is more important than temporal information (speed and acceleration) for detecting decision changes, and we offer a comparison of the sensitivities of various typical measures used in analyses of mouse tracking (area under the trajectory curve, direction flips, etc.). We do so using an “optimal” analysis of our data (a linear discriminant analysis explicitly trained to classify trajectories) and see what type of data (position, speed, or acceleration) it capitalizes on. We also quantify how its results compare with those based on more standard measures.
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Keyword:
[SCCO.LING]Cognitive science/Linguistics; [SCCO.PSYC]Cognitive science/Psychology; Decision making; LDA; Mouse tracking; Negation processing; Sentence verification
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URL: https://doi.org/10.3758/s13428-018-01194-x https://hal.archives-ouvertes.fr/hal-02274523/document https://hal.archives-ouvertes.fr/hal-02274523/file/MaldonadoDunbarChemla_paper.pdf https://hal.archives-ouvertes.fr/hal-02274523
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The Zero Resource Speech Challenge 2017
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In: ASRU 2017 ; https://hal.inria.fr/hal-01687504 ; ASRU 2017, Dec 2017, Okinawa, Japan (2017)
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Learning Weakly Supervised Multimodal Phoneme Embeddings
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In: Interspeech 2017 ; https://hal.inria.fr/hal-01687415 ; Interspeech 2017, 2017, Stockholm, Sweden. ⟨10.21437/Interspeech.2017-1689⟩ (2017)
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Classification and automatic transcription of primate calls
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In: ISSN: 0001-4966 ; EISSN: 1520-8524 ; Journal of the Acoustical Society of America ; https://hal.archives-ouvertes.fr/hal-02474093 ; Journal of the Acoustical Society of America, Acoustical Society of America, 2016, 140 (1), pp.EL26-EL30. ⟨10.1121/1.4954887⟩ (2016)
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