SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition
This work addresses the problem of limited labeled data for sign language recognition, which is crucial for improving communication tools for people with hearing loss, but it appears incremental as it applies existing semi-supervised techniques to this domain.
The paper tackles the challenge of scarce annotated datasets in sign language recognition by proposing a semi-supervised learning method that uses pseudo-labeling and pose information with a Transformer backbone. The results show that this approach outperforms fully supervised models with less labeled data on the WLASL-100 dataset.
Sign language is the primary communication language for people with disabling hearing loss. Sign language recognition (SLR) systems aim to recognize sign gestures and translate them into spoken language. One of the main challenges in SLR is the scarcity of annotated datasets. To address this issue, we propose a semi-supervised learning (SSL) approach for SLR (SSLR), employing a pseudo-label method to annotate unlabeled samples. The sign gestures are represented using pose information that encodes the signer's skeletal joint points. This information is used as input for the Transformer backbone model utilized in the proposed approach. To demonstrate the learning capabilities of SSL across various labeled data sizes, several experiments were conducted using different percentages of labeled data with varying numbers of classes. The performance of the SSL approach was compared with a fully supervised learning-based model on the WLASL-100 dataset. The obtained results of the SSL model outperformed the supervised learning-based model with less labeled data in many cases.