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A Pose-Based Pipeline for Annotation of Headshakes in Sign Language Corpora

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.