| The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling TA Nguyen*, M de Seyssel*, P Rozé, M Rivière, E Kharitonov, A Baevski, ... arXiv preprint arXiv:2011.11588, 2020 | 96 | 2020 |
| The zero resource speech challenge 2021: Spoken language modelling E Dunbar, M Bernard, N Hamilakis, TA Nguyen, M De Seyssel, P Rozé, ... arXiv preprint arXiv:2104.14700, 2021 | 45 | 2021 |
| Probing phoneme, language and speaker information in unsupervised speech representations M de Seyssel, M Lavechin, Y Adi, E Dupoux, G Wisniewski arXiv preprint arXiv:2203.16193, 2022 | 27 | 2022 |
| Reverse engineering language acquisition with child-centered long-form recordings M Lavechin, M De Seyssel, L Gautheron, E Dupoux, A Cristia Annual Review of Linguistics 8 (1), 389-407, 2022 | 21 | 2022 |
| Statistical learning bootstraps early language acquisition M Lavechin*, M de Seyssel*, H Titeux, H Bredin, G Wisniewski, A Cristia, ... PsyArXiv, 2022 | 9 | 2022 |
| Are word boundaries useful for unsupervised language learning? TA Nguyen, M De Seyssel, R Algayres, P Roze, E Dunbar, E Dupoux arXiv preprint arXiv:2210.02956, 2022 | 7 | 2022 |
| Statistical learning models of early phonetic acquisition struggle with child-centered audio data M Lavechin, M De Seyssel, M Métais, F Metze, A Mohamed, H Bredin, ... PsyArXiv, 2022 | 7 | 2022 |
| ProsAudit, a prosodic benchmark for self-supervised speech models M de Seyssel, M Lavechin, H Titeux, A Thomas, G Virlet, AS Revilla, ... arXiv preprint arXiv:2302.12057, 2023 | 4 | 2023 |
| The specificity of sequential Statistical Learning: Statistical Learning accumulates predictive information from unstructured input but is dissociable from (declarative) memory A Endress, M De Seyssel Available at SSRN 4631864, 2023 | 3 | 2023 |
| Investigating the usefulness of i-vectors for automatic language characterization M de Seyssel, G Wisniewski, E Dupoux, B Ludusan Proc. Speech Prosody 2022, 460-464, 2022 | 3 | 2022 |
| Does bilingual input hurt? a simulation of language discrimination and clustering using i-vectors M de Seyssel, E Dupoux Cogsci 2020-42nd annual virtual meeting of the cognitive science society, 2020 | 3 | 2020 |
| Modeling early phonetic acquisition from child-centered audio data M Lavechin, M de Seyssel, M Métais, F Metze, A Mohamed, H Bredin, ... Cognition 245, 105734, 2024 | 2 | 2024 |
| EmphAssess: a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models M de Seyssel, A D'Avirro, A Williams, E Dupoux arXiv preprint arXiv:2312.14069, 2023 | 2 | 2023 |
| Realistic and broad-scope learning simulations: first results and challenges M de SEYSSEL, M Lavechin, E Dupoux Journal of Child Language 50 (6), 1294-1317, 2023 | 2 | 2023 |
| Statistical learning bootstraps early language acquisition M Lavechin, M De Seyssel, E Dupoux | 2 | 2023 |
| Is the Language Familiarity Effect gradual? A computational modelling approach M de Seyssel, G Wisniewski, E Dupoux arXiv preprint arXiv:2206.13415, 2022 | 1 | 2022 |
| Qwant Research@ DEFT 2019: appariement de documents et extraction d’informations à partir de cas cliniques (Document matching and information retrieval using clinical cases) E Maudet, O Cattan, M de Seyssel, C Servan Actes de la Conférence sur le Traitement Automatique des Langues Naturelles …, 2019 | 1* | 2019 |
| The limits of statistical learning in word segmentation: Accumulation of predictive information from unstructured input in the absence of (declarative) memory A Endress, M de Seyssel PsyArXiv, 2022 | | 2022 |
| “GODE RU” or “GO DERU”? The Role of Statistical and Crosslinguistic Prosodic Cues in Segmenting Groups of Words M de Seyssel City, University of London, 2016 | | 2016 |
| 4.3 Group 2.1: Embodied Intention Prediction Challenge M Frank, N Kunda, M Lavechin, PY Oudeyer, R Saxe, M de Seyssel, ... Developmental Machine Learning: From Human Learning to Machines and Back, 162, 0 | | |
Emmanuel DupouxProfessor of Cognitive Psychology, Ecole des Hautes Etudes en Sciences Sociales, Paris在 ehess.fr 的电子邮件经过验证
