@inproceedings{saenz-2022-mouthing,
title = "Mouthing Recognition with {O}pen{P}ose in Sign Language",
author = "Saenz, Maria Del Carmen",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
McDonald, John C. and
Shterionov, Dimitar and
Wolfe, Rosalee",
booktitle = "Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.sltat-1.14",
pages = "91--94",
abstract = "Many avatars focus on the hands and how they express sign language. However, sign language also uses mouth and face gestures to modify verbs, adjectives, or adverbs; these are known as non-manual components of the sign. To have a translation system that the Deaf community will accept, we need to include these non-manual signs. Just as machine learning is being used on generating hand signs, the work we are focusing on will be doing the same, but with mouthing and mouth gestures. We will be using data from The National Center for Sign Language and Gesture Resources. The data from the center are videos of native signers focusing on different areas of signer movement, gesturing, and mouthing, and are annotated specifically for mouthing studies. With this data, we will run a pre-trained Neural Network application called OpenPose. After running through OpenPose, further analysis of the data is conducted using a Random Forest Classifier. This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy. With this, the appropriate mouthing for animated signs can be easily applied to avatar technologies.",
}
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<abstract>Many avatars focus on the hands and how they express sign language. However, sign language also uses mouth and face gestures to modify verbs, adjectives, or adverbs; these are known as non-manual components of the sign. To have a translation system that the Deaf community will accept, we need to include these non-manual signs. Just as machine learning is being used on generating hand signs, the work we are focusing on will be doing the same, but with mouthing and mouth gestures. We will be using data from The National Center for Sign Language and Gesture Resources. The data from the center are videos of native signers focusing on different areas of signer movement, gesturing, and mouthing, and are annotated specifically for mouthing studies. With this data, we will run a pre-trained Neural Network application called OpenPose. After running through OpenPose, further analysis of the data is conducted using a Random Forest Classifier. This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy. With this, the appropriate mouthing for animated signs can be easily applied to avatar technologies.</abstract>
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%0 Conference Proceedings
%T Mouthing Recognition with OpenPose in Sign Language
%A Saenz, Maria Del Carmen
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y McDonald, John C.
%Y Shterionov, Dimitar
%Y Wolfe, Rosalee
%S Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F saenz-2022-mouthing
%X Many avatars focus on the hands and how they express sign language. However, sign language also uses mouth and face gestures to modify verbs, adjectives, or adverbs; these are known as non-manual components of the sign. To have a translation system that the Deaf community will accept, we need to include these non-manual signs. Just as machine learning is being used on generating hand signs, the work we are focusing on will be doing the same, but with mouthing and mouth gestures. We will be using data from The National Center for Sign Language and Gesture Resources. The data from the center are videos of native signers focusing on different areas of signer movement, gesturing, and mouthing, and are annotated specifically for mouthing studies. With this data, we will run a pre-trained Neural Network application called OpenPose. After running through OpenPose, further analysis of the data is conducted using a Random Forest Classifier. This research looks at how well an algorithm can be trained to spot certain mouthing points and output the mouth annotations with a high degree of accuracy. With this, the appropriate mouthing for animated signs can be easily applied to avatar technologies.
%U https://aclanthology.org/2022.sltat-1.14
%P 91-94
Markdown (Informal)
[Mouthing Recognition with OpenPose in Sign Language](https://aclanthology.org/2022.sltat-1.14) (Saenz, SLTAT 2022)
ACL
- Maria Del Carmen Saenz. 2022. Mouthing Recognition with OpenPose in Sign Language. In Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives, pages 91–94, Marseille, France. European Language Resources Association.