Aurélie Nardy
Associate Professor
aurelie.nardy@univ-grenoble-alpes.fr
Margot Masson
Associate Member
margot.masson@univ-grenoble-alpes.fr
Technological advances in the collection of large-scale learner data offer a new opportunity to improve students’ learning conditions, requiring the use of AI to enrich, analyze, and model this data. Using the DyLNet database, which encompasses 2.5 years of preschool social and verbal interactions (between children and adults), this project addresses three key challenges: dynamically visualizing social interaction networks; developing an automatic transcription tool for spontaneous child speech that enables the assessment of language development levels; and modeling the dynamic network of interactions and its reciprocal influences on language development. This massive database offers a unique opportunity to develop AI-based analyses and tools that would enable a significant qualitative leap in understanding the mechanisms of early socialization and its link to language, with the goal of reducing social and educational inequalities.
The chair will recruit a Ph.D. student to develop tools that researchers in the social sciences and humanities can use to dynamically visualize and explore social interaction networks in a preschool setting.
The chair will also hire a postdoctoral researcher for an 18-month term to extract relevant information for identifying representative indicators of child language development and to implement them in a tool that both automatically transcribes children’s speech and assesses their level of language development.
Finally, a 24-month postdoctoral researcher will be hired to work on modeling the dynamic network of school interactions and its reciprocal influences on language use.
Blanch, R. (2023). BiVis: interactive and progressive visualization of billions (and counting) of items. In Proceedings of the 19th IFIP TC13 International Conference (Interact 2023). https://doi.org/10.1007/978-3-031-42283-6_4
Blanch, R., Lecointre, A., Ortega, M., Roux, P. (2023). Visualization of Spatio-Temporal Data: A Seismological Study of the Argentière Glacier. In Proceedings of the Visu 2023 Conference. https://journee-visu.github.io/2023/submissions/Blanch_et_al.pdf
Coulange, S., Kato, T., Rossato, S., & Masperi, M. (2024). Enhancing language learners’ comprehensibility through automated analysis of pause positions and syllable prominence. Languages, 9(3), 78. https://doi.org/10.3390/languages9030078
Dai, S., Bouchet, H., Karsai, M., Chevrot, J.-P., Fleury, E., Nardy, A. (2022). Longitudinal data collection to track the dynamics of social network and language development in preschool. Scientific Data, 9, Article 777. https://doi.org/10.1038/s41597-022-01756-x
Dai, S., Bouchet, H., Nardy, A., Fleury, E., Chevrot, J.-P., Karsai, M. (2020). Temporal social network reconstruction using wireless proximity sensors: model selection and consequences. EPJ Data Science, 9, Article 19. https://doi.org/10.1140/epjds/s13688-020-00237-8
Evain, S., Rossato, S., Portet, F. (2024). Unraveling spontaneous speech dimensions for cross-corpus ASR system evaluation for French. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 17165–17175, Turin, Italy. https://aclanthology.org/2024.lrec-main.1491.pdf
Havard, W. N., Govain, R., Gonçalves Teixeira, D., Lecouteux, B., Schang, E. (2024). Speech Technologies and Field Data: The Case of Haitian Creole. In Proceedings of the 31st Conference on Natural Language Processing, Volume 1: Full Papers and Position Papers, pp. 686–694, Toulouse, France. ATALA and AFPC. https://aclanthology.org/2024.jeptalnrecital-taln.45.pdf
Mouchené, M., Blanch, R., Pathier, E., Montet, R., Thollard, F. (2024). InsarViz: an open-source Python package for the interactive visualization of satellite SAR interferometry data. Journal of Open Source Software, 9(101), 6440. https://doi.org/10.21105/joss.06440
Parcollet, T., Nguyen, H., Evain, S., Boito, M. Z., Pupier, A., Mdhaffar, S., Le, H., Alisamir, S., Tomashenko, N., Dinarelli, M., Zhang, S., Allauzen, A., Coavoux, M., Esteve, Y., Rouvier, M., Goulian, J., Lecouteux, B., Portet, F., Rossato, S., Ringeval, F., Schwab, D., Besacier, L. (2024). LeBenchmark 2.0: a Standardized, Replicable, and Enhanced Framework for Self-supervised Representations of French Speech. Computer Speech & Language, 86, 101622. https://doi.org/10.1016/j.csl.2024.101622
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