Maureen de Seyssel
Maureen de Seyssel
CoML team, LSCP, Ecole Normale Supérieure
Verified email at - Homepage
Cited by
Cited by
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
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
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, 389-407, 2022
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
Statistical learning bootstraps early language acquisition
M Lavechin*, M de Seyssel*, H Titeux, H Bredin, G Wisniewski, A Cristia, ...
PsyArXiv, 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, 2023
Are word boundaries useful for unsupervised language learning?
TA Nguyen, M De Seyssel, R Algayres, P Rozé, E Dunbar, E Dupoux
arXiv preprint arXiv:2210.02956, 2022
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
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
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
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
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
Realistic and broad-scope learning simulations: first results and challenges
M de SEYSSEL, M Lavechin, E Dupoux
Journal of Child Language, 1-24, 2023
Is the Language Familiarity Effect gradual? A computational modelling approach
M de Seyssel, G Wisniewski, E Dupoux
arXiv preprint arXiv:2206.13415, 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
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
Given what babies really hear, both domain-general and domain-specific mechanisms are needed to simulate perceptual attunement
M Lavechin, M de Seyssel, M Métais, F Metze, A Mohamed, H Bredin, ...
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