Lexicon Induction from Continuous Sign Language Corpus

Overview

lexicon-induction is a project aimed at inducing a lexicon from continuous sign language corpora. The project leverages large datasets of sign language videos, pose estimation technologies, sign language segmentation tools, and machine learning models to analyze and categorize sign language data.

Datasets Used

• DGS Corpus¹ - Development of a corpus-based electronic dictionary German Sign Language/German.
• Corpus NGT² - An online corpus for professionals and laymen in Dutch Sign Language (NGT).
• BSL Corpus³ - Building the British Sign Language Corpus.

Workflow

1. Pose Estimation

For each video in corpus/videos, we run pose estimation:

video_to_pose -i sign.mp4 --format mediapipe -o sign.pose

Output pose files are stored in poses.

2. Segmentation

Pose sequences are automatically segmented using the sign language segmentation tool:

pose_to_segments -i sign.pose -o sign.eaf --video sign.mp4

Segmentation outputs (ELAN files) are stored in segments.

3. Sign Language Recognition

For each segment in the ELAN file:

• Crop the corresponding pose.
• Run through a sign language recognition model.

We focus on the softmax layer output for label distributions.

4. Clustering

• Signs are clustered based on softmax output vectors.
• Assumes a Zipfian distribution over sign usage (evidence)
• Cluster sizes reflect this distribution.

5. Evaluation

• Evaluated on the pre-annotated DGS Corpus.
• Metrics relevant for clustering under a Zipfian distribution are used.

References

Footnotes

1. Prillwitz, Siegmund, et al. "DGS Corpus project--development of a corpus based electronic dictionary German Sign Language/German." Sign-lang at LREC. 2008. ↩

2. Crasborn, Onno, and Inge Zwitserlood. "The Corpus NGT: An online corpus for professionals and laymen." 2008. ↩

3. Schembri, Adam, et al. "Building the British sign language corpus." Language Documentation & Conservation, vol. 7, 2013, pp. 136-154. ↩