Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition

Polina Yanovich, Carol Neidle, Dimitris Metaxas

Abstract
In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker.
Anthology ID:
L16-1490
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3067–3073
Language:
URL:
https://aclanthology.org/L16-1490
DOI:
Bibkey:
Cite (ACL):
Polina Yanovich, Carol Neidle, and Dimitris Metaxas. 2016. Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3067–3073, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition (Yanovich et al., LREC 2016)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/L16-1490.pdf
PDF Search