A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News

TL;DR

This paper presents TVB-HKSL-News, a comprehensive Hong Kong sign language dataset from TV news, supporting research in large-vocabulary sign language recognition and translation, with automated data collection and baseline performance metrics.

Contribution

Introduces a large-scale, automated Hong Kong sign language dataset from TV news to facilitate research in sign language recognition and translation.

Findings

Baseline SLR word error rate of 34.08%

Baseline SLT BLEU-4 score of 23.58

Dataset includes 16.07 hours of sign videos with 6,515 glosses

Abstract

This paper introduces TVB-HKSL-News, a new Hong Kong sign language (HKSL) dataset collected from a TV news program over a period of 7 months. The dataset is collected to enrich resources for HKSL and support research in large-vocabulary continuous sign language recognition (SLR) and translation (SLT). It consists of 16.07 hours of sign videos of two signers with a vocabulary of 6,515 glosses (for SLR) and 2,850 Chinese characters or 18K Chinese words (for SLT). One signer has 11.66 hours of sign videos and the other has 4.41 hours. One objective in building the dataset is to support the investigation of how well large-vocabulary continuous sign language recognition/translation can be done for a single signer given a (relatively) large amount of his/her training data, which could potentially lead to the development of new modeling methods. Besides, most parts of the data collection pipeline are automated with little human intervention; we believe that our collection method can be scaled up to collect more sign language data easily for SLT in the future for any sign languages if such sign-interpreted videos are available. We also run a SOTA SLR/SLT model on the dataset and get a baseline SLR word error rate of 34.08% and a baseline SLT BLEU-4 score of 23.58 for benchmarking future research on the dataset.