Papers by Sandrine Tornay
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition (2020.lrec-1)
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| Challenge: | despite the extensive use of HMMs for sign recognition, determining the HMM structure remains a challenge . despite their success in modeling sequential and multivariate data, establishing the structure remains challenging . |
| Approach: | They propose a continuous HMM framework for modeling and recognizing isolated signs . they propose to optimize the number of states for each sign separately during recognition . |
| Outcome: | The proposed model performs better on three different datasets and is competitive with existing models. |
SMILE Swiss German Sign Language Dataset (L18-1)
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Sarah Ebling, Necati Cihan Camgöz, Penny Boyes Braem, Katja Tissi, Sandra Sidler-Miserez, Stephanie Stoll, Simon Hadfield, Tobias Haug, Richard Bowden, Sandrine Tornay, Marzieh Razavi, Mathew Magimai-Doss
| Challenge: | The goal of an ongoing three-year project in Switzerland is to pioneer an assessment system for lexical signs of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) that relies on sign language recognition. |
| Approach: | The goal of the project is to pioneer an assessment system for lexical signs of Swiss German Sign Language that relies on sign language recognition. |
| Outcome: | The system will give adult L2 learners of DSGS feedback on the correctness of the manual parameters (handshape, hand position, location, and movement) of isolated signs they produce. |