@inproceedings{gren-riemer-kankkonen-2026-pose,
title = "A Pose-Based Pipeline for Annotation of Headshakes in Sign Language Corpora",
author = "Gren, Gustaf and
Riemer Kankkonen, Nikolaus",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.signlang-1.21/",
doi = "10.63317/4g5mwahb527o",
pages = "193--202",
abstract = "This paper introduces a pose-based pipeline designed to support scalable annotation of headshakes in sign language corpora. Motivated by the scarcity of annotated datasets and the need for quantitative typological research, the study evaluates whether automated detection can reduce human annotation effort. The system operates on yaw trajectories extracted with MediaPipe Holistic and uses sliding-window segmentation with neural sequence models (LSTM/CNN) to surface candidate segments for review. Training and evaluation are conducted on a subset of the German Sign Language (DGS) Corpus annotated to target grammatical headshakes functioning as negation rather than for every instance of headshakes. On the DGS dataset the best performing LSTM model achieves an F2-score of 0.45, recall of 0.63. Despite the narrow annotation scope, the pipeline reduces search space: annotators need review only 13{\%} of frames to recover 87{\%} of labeled instances. Error analysis indicates that many false positives correspond to plausible head movements excluded by the annotation criteria. A pilot transfer to Swedish Sign Language shows reduced effectiveness without adaptation, underscoring the need for alignment in cross-lingual transfer scenarios."
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<abstract>This paper introduces a pose-based pipeline designed to support scalable annotation of headshakes in sign language corpora. Motivated by the scarcity of annotated datasets and the need for quantitative typological research, the study evaluates whether automated detection can reduce human annotation effort. The system operates on yaw trajectories extracted with MediaPipe Holistic and uses sliding-window segmentation with neural sequence models (LSTM/CNN) to surface candidate segments for review. Training and evaluation are conducted on a subset of the German Sign Language (DGS) Corpus annotated to target grammatical headshakes functioning as negation rather than for every instance of headshakes. On the DGS dataset the best performing LSTM model achieves an F2-score of 0.45, recall of 0.63. Despite the narrow annotation scope, the pipeline reduces search space: annotators need review only 13% of frames to recover 87% of labeled instances. Error analysis indicates that many false positives correspond to plausible head movements excluded by the annotation criteria. A pilot transfer to Swedish Sign Language shows reduced effectiveness without adaptation, underscoring the need for alignment in cross-lingual transfer scenarios.</abstract>
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%0 Conference Proceedings
%T A Pose-Based Pipeline for Annotation of Headshakes in Sign Language Corpora
%A Gren, Gustaf
%A Riemer Kankkonen, Nikolaus
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F gren-riemer-kankkonen-2026-pose
%X This paper introduces a pose-based pipeline designed to support scalable annotation of headshakes in sign language corpora. Motivated by the scarcity of annotated datasets and the need for quantitative typological research, the study evaluates whether automated detection can reduce human annotation effort. The system operates on yaw trajectories extracted with MediaPipe Holistic and uses sliding-window segmentation with neural sequence models (LSTM/CNN) to surface candidate segments for review. Training and evaluation are conducted on a subset of the German Sign Language (DGS) Corpus annotated to target grammatical headshakes functioning as negation rather than for every instance of headshakes. On the DGS dataset the best performing LSTM model achieves an F2-score of 0.45, recall of 0.63. Despite the narrow annotation scope, the pipeline reduces search space: annotators need review only 13% of frames to recover 87% of labeled instances. Error analysis indicates that many false positives correspond to plausible head movements excluded by the annotation criteria. A pilot transfer to Swedish Sign Language shows reduced effectiveness without adaptation, underscoring the need for alignment in cross-lingual transfer scenarios.
%R 10.63317/4g5mwahb527o
%U https://aclanthology.org/2026.signlang-1.21/
%U https://doi.org/10.63317/4g5mwahb527o
%P 193-202
Markdown (Informal)
[A Pose-Based Pipeline for Annotation of Headshakes in Sign Language Corpora](https://aclanthology.org/2026.signlang-1.21/) (Gren & Riemer Kankkonen, SignLang 2026)
ACL