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pro vyhledávání: '"Astrid Rybner"'
Autor:
Astrid Rybner, Emil Trenckner Jessen, Marie Damsgaard Mortensen, Stine Nyhus Larsen, Ruth Grossman, Niels Bilenberg, Cathriona Cantio, Jens Richardt Møllegaard Jepsen, Ethan Weed, Arndis Simonsen, Riccardo Fusaroli
Publikováno v:
Rybner, A, Jessen, E T, Mortensen, M D, Larsen, S N, Grossman, R, Bilenberg, N, Cantio, C, Jepsen, J R M, Weed, E, Simonsen, A & Fusaroli, R 2022, ' Vocal markers of autism : Assessing the generalizability of machine learning models ', Autism Research, vol. 15, no. 6, pp. 1018-1030 . https://doi.org/10.1002/aur.2721
Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b0310ef18ff6b8f96b37d95c767945e
https://doi.org/10.1002/aur.2721
https://doi.org/10.1002/aur.2721
Autor:
Cathriona Cantio, Niels Bilenberg, Jens Richardt Møllegaard Jepsen, Ethan Weed, Marie Mortensen, Arndis Simonsen, Riccardo Fusaroli, Ruth B. Grossman, Emil Trenckner Jessen, Astrid Rybner, Stine Nyhus Larsen
Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected e.g., using a different speech task or in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d8e5a59ec64a277a0c6331eae4ccac1
https://doi.org/10.1101/2021.11.22.469538
https://doi.org/10.1101/2021.11.22.469538
