Abstract
Inspired by recent developments in natural language processing, we propose a novel approach to sign language processing based on phonological properties validated by American Sign Language users. By taking advantage of datasets composed of phonological data and people speaking sign language, we use a pretrained deep model based on mesh reconstruction to extract the 3D coordinates of the signers keypoints. Then, we train standard statistical and deep machine learning models in order to assign phonological classes to each temporal sequence of coordinates. Our paper introduces the idea of exploiting the phonological properties manually assigned by sign language users to classify videos of people performing signs by regressing a 3D mesh. We establish a new baseline for this problem based on the statistical distribution of 725 different signs. Our best-performing models achieve a micro-averaged F1-score of 58% for the major location class and 70% for the sign type using statistical and deep learning algorithms, compared to their corresponding baselines of 35% and 39%.
Original language | English |
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Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings |
Publisher | IEEE |
Pages | 8452-8456 |
Number of pages | 5 |
ISBN (Electronic) | 9781665405409 |
DOIs | |
Publication status | Published - 27 Apr 2022 |
Event | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2022-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 |
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Country/Territory | Singapore |
City | Virtual, Online |
Period | 23/05/22 → 27/05/22 |
Keywords
- machine learning
- Phonology
- RGB
- sign language
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RAI: Centre for Robotics and Artificial Intelligence
Cangelosi, A. (PI), Lennox, B. (PI), Weightman, A. (CoI), Dennis, L. (CoI), Dixon, C. (CoI), Fisher, M. (CoI), Herrmann, G. (CoI), Dickson, A. (CoI), Lanzon, A. (CoI), Stancu, A. (CoI), Miller, A. (CoI), Freitas, A. (CoI), Nini, A. (CoI), Voronkov, A. (CoI), Brass, A. (CoI), Vijayaraghavan, A. (CoI), Parslew, B. (CoI), crowther, W. (CoI), Grieve, B. (CoI), Adorno, B. (CoI), Jay, C. (CoI), Wang, C. C. L. (CoI), Todd, C. (CoI), Soutis, C. (CoI), Arsene, C. (CoI), Dresner, D. (CoI), Barrett, E. (CoI), Gowen, E. (CoI), Arvin, F. (CoI), Podd, F. (CoI), Brown, G. (CoI), Reger, G. (CoI), Cooper, G. (CoI), Mairs, H. (CoI), Yin, H. (CoI), Kinloch, I. (CoI), Eleftheriou, I. (CoI), Li, J. (CoI), Carrasco Gomez, J. (CoI), Ainsworth, J. (CoI), Sinha, J. (CoI), Ozanyan, K. (CoI), Smith, K. (CoI), Twomey, K. (CoI), Margetts, L. (CoI), Ren, L. (CoI), Zhang, L. (CoI), Cordeiro, L. (CoI), Rattray, M. (PI), Bissett, M. (CoI), Elliot, M. (CoI), Alvarez, M. (CoI), Luján, M. (CoI), Nabawy BSc, MSc, PhD, MRAeS, SMAIAA, FHEA, M. (CoI), Peek, N. (CoI), Marjanovic, O. (CoI), Dorn, O. (CoI), Dudek, P. (CoI), Green, P. (CoI), Connolly, P. (CoI), Da Silva Bartolo, P. J. (CoI), Gardner, P. (CoI), Martin, P. (CoI), Potluri, P. (CoI), Curtis, R. (CoI), Schmidt, R. (CoI), Banach, R. (CoI), Batista-Navarro, R. T. (CoI), Kaski, S. (CoI), Midson, S. (CoI), Watson, S. (CoI), Holm, S. (CoI), Furber, S. (CoI), Schlegel, V. (CoI), Mirihanage, W. (CoI), Mansell, W. (CoI), Pan, W. (CoI), Sampson, W. (CoI), Sellers, W. (CoI), Yang, W. (CoI), Cai, P. (CoI), Sun, Y. (CoI), Alharthi, A. (Researcher), Macario Rojas, A. (Researcher), Serhan, B. (Researcher), Yu, C. (Researcher), Abara, D. (Researcher), Lopez Pulgarin, E. (Researcher), Faruq, F. (Researcher), Tavella, F. (Researcher), Semeraro, F. (Researcher), Liu, G. (Researcher), Fang, G. (Researcher), Niu, H. (Researcher), Taylor, H. (PI), Zhu, H. (PGR student), Collenette, J. (Researcher), Amano, K. (Researcher), Lo, K. C. J. (PGR student), Raggioli, L. (Researcher), Romeo, M. (Researcher), Ruocco, M. (PGR student), Ghaffari Saadat, M. (Researcher), Walmsley, M. (Researcher), Mubarik, A. (Researcher), Vinanzi, S. (Researcher), Su, Y.-H. (PGR student), McAleese, H. (PGR student), Stringer, P. (PGR student), Stoican, R. (PGR student), Ye, R. (PGR student), Kurawa, S. S. (PGR student), Zhang, T. (PGR student), Krywonos, W. (PGR student), Xu, Y. (PGR student), Tian, Y. (PGR student), Henderson, A. (Technical team), Morley, D. (Support team), Tallentire, J. (Support team), Smith, J. (Support team), Hawthornthwaite, S. (Support team), Carlson, J. (Support team) & Baniqued, P. D. (Researcher)
Project: Research