Selected Publications

For the full list of publications, see my Google Scholar profile.

* denotes equal contribution as first author.

de Seyssel, M., D'Avirro, A., Williams, A., & Dupoux, E. (2024). EmphAssess: a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models. arXiv preprint arXiv:2312.14069. [pdf]

de Seyssel, M.*, Lavechin, M.*, & Dupoux, E. (2023) Realistic and broad-scope learning simulations: First results and challenges. Journal of Child Language, 1-24. doi:10.1017/S0305000923000272 [pdf]

de Seyssel, M., Lavechin, M., Titeux, H., Thomas, A., Virlet, G., Santos Revilla, A., Wisniewski, G., Ludusan, B., & Dupoux, E. (2023) ProsAudit, a prosodic benchmark for self-supervised speech models. In Proc. Interspeech 2023. [pdf] [benchmark] [leaderboard]

Lavechin, M.*, de Seyssel, M.*, Titeux, H., Bredin, H., Wisniewski, G., Cristia, A., & Dupoux, E. (2022) Can statistical learning bootstrap early language acquisition? A modeling investigation.  Retrieved from [pdf] [video]

de Seyssel, M., Lavechin, M., Adi, Y., Dupoux, E., & Wisniewski, G. (2022) Probing phoneme, language and speaker information in unsupervised speech representations. In Proc. Interspeech 2022, 1402-1406, doi: 10.21437/Interspeech.2022-373 [pdf] [video] [poster]

Endress, A., & de Seyssel, M. (2022). The limits of statistical learning in word segmentation: Accumulation of predictive information from unstructured input in the absence of (declarative) memory. Retrieved from [pdf]

de Seyssel, M., Wisniewski, G., & Dupoux, E. (2022) Is the Language Familiarity Effect gradual ? A computational modelling approach. In Proceedings for the Annual Meeting of the Cognitive Science Society 2022 [pdf] [poster]

de Seyssel, M., Wisniewski, G., Dupoux, E., & Ludusan, B. (2022). Investigating the usefulness of i-vectors for automatic language characterization. In Proc. Speech Prosody 2022, 460-464, doi: 10.21437/SpeechProsody.2022-94 [pdf] [poster]

Nguyen, T. A.*, de Seyssel, M.*, Rozé, P., Rivière, M., Kharitonov, E., Baevski, A., Dunbar, E., & Dupoux,E. (2020). The zero resource speech benchmark 2021: Metrics and baselines for unsupervised spoken language modeling. In Neurips Workshop on Self-Supervised Learning for Speech and Audio Processing. [pdf] [video]

de Seyssel, M. & Dupoux, E. (2020). Does bilingual input hurt? A simulation of language discrimination and clustering using i-vectors. In Proceedings for the Annual Meeting of the Cognitive Science Society 2020 [pdf] [poster]

Academic dissertations

de Seyssel, M. (2023). Unsupervised multilingual models of speech representation, an approach inspired by cognitive science. [PhD Dissertation]. Ecole Normale Supérieure. [pdf]

de Seyssel, M. (2017). Active learning for training data selection for Automatic Speech Recognition using Unreliable Transcriptions. [Unpublished MSc Dissertation]. University of Edinburgh. (restricted access rights - manuscript available on demand)

de Seyssel, M. (2016). The Role of Statistical and Crosslinguistic Prosodic Cues in Segmenting Groups of Words. [Unpublished BSc Dissertation]. City, University of London. [pdf]

Other publications

Lavechin, M., de Seyssel, M., Métais, M., Metze, F., Mohamed, A., Bredin, H., Dupoux, E. & Cristia, A. (2023). Statistical learning models of early phonetic acquisition struggle with child-centered audio data. Retrieved from [full article] [preprint]

Lavechin, M., de Seyssel, M.,  Gautheron, L., Dupoux, E., & Cristia, A. (2021). Reverse-engineering language acquisition with child-centered long-form recordings. Annual Review of Linguistics, 8, 389-407. [pdf]

Dunbar, E., Bernard, M., Hamilakis, N., Nguyen, T.A., de Seyssel, M., Rozé, P., Rivière, M., Kharitonov, E. & Dupoux, E. (2021). The Zero Resource Speech Challenge 2021: Spoken Language Modelling. In Proc. Interspeech 2021, 1574-1578, doi: 10.21437/Interspeech.2021-1755. [pdf]

Nguyen, T.A., de Seyssel, M., Algayres, R., Roze, P., Dunbar, E., Dupoux, E. (2020). Are word boundaries useful for unsupervised language learning? CoML Technical Report, September 2020. [pdf]

Maudet, E., Cattan, O., de Seyssel, M., & Servan, C. (2019). Qwant Research@ DEFT 2019: appariement de documents et extraction d’informations à partir de cas cliniques (Document matching and information retrieval using clinical cases). In Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Défi Fouille de Textes (atelier TALN-RECITAL) (pp. 67-80). [pdf]

Get in touch

maureen.deseyssel (at)