Natural language recognition techniques can be applied not only to speech signals, but to other signals that represent natural language units (e.g., words and sentences). This is the case of sign language recognition, which is usually employed by deaf people to communicate. The use of recognition techniques may allow this language users to communicate more independently with non-signal users. Several works have been done for different variants of sign languages, but in most cases their vocabulary is quite limited and they only recognise gestures corresponding to isolated words. In this work, we propose gesture recognisers which make use of typical Continuous Density Hidden Markov Model. They solve not only the isolated word problem, but also the recognition of basic sentences using the Spanish Sign Language with a higher vocabulary than in other approximations. Different topologies and Gaussian mixtures are studied. Results show that our proposal provides promising results that are the first step to obtain a general automatic recognition of Spanish Sign Language.
Cite as: Martínez-Hinarejos, C.-D., Parcheta, Z. (2017) Spanish Sign Language Recognition with Different Topology Hidden Markov Models. Proc. Interspeech 2017, 3349-3353, doi: 10.21437/Interspeech.2017-275
@inproceedings{martinezhinarejos17_interspeech,
author={Carlos-D. Martínez-Hinarejos and Zuzanna Parcheta},
title={{Spanish Sign Language Recognition with Different Topology Hidden Markov Models}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={3349--3353},
doi={10.21437/Interspeech.2017-275}
}