Large-Scale Hong Kong Sign Language Dataset for Continuous Sign Language Recognition and Translation

Core Concepts

This paper introduces a new large-scale Hong Kong Sign Language (HKSL) dataset, TVB-HKSL-News, collected from sign-interpreted TV news programs. The dataset aims to support research in large-vocabulary continuous sign language recognition and translation.

Abstract

The TVB-HKSL-News dataset was collected from the "TVB News Report with Sign Language" program in Hong Kong over a 7-month period. It consists of 16.07 hours of sign videos from two signers, with a vocabulary of 6,515 glosses for sign language recognition (SLR) and 2,850 Chinese characters or 18K words for sign language translation (SLT).
The key highlights of the dataset are:

It is the largest publicly available HKSL dataset, supporting research in large-vocabulary continuous SLR and SLT.
The data collection pipeline is largely automated, making it scalable to collect more sign language data from sign-interpreted videos.
The dataset includes both sign video clips and their corresponding Chinese subtitles, as well as professional annotations of sign glosses.
The dataset enables the investigation of signer-dependent SLR and SLT, with a large amount of data for a single signer.

The authors also provide baseline results using state-of-the-art SLR and SLT models. The best-performing model achieves a word error rate of 34.08% on SLR and a BLEU-4 score of 23.58 on SLT. Further analysis shows that the amount of training data has a significant impact on SLT performance, while SLR performance converges with around 8 hours of training data for a single signer.

Stats

The dataset contains 16.07 hours of sign videos from two signers.
The vocabulary size is 6,515 glosses for SLR and 2,850 Chinese characters or 18K words for SLT.
One signer contributes 11.66 hours of videos, while the other contributes 4.41 hours.
The average duration of sign video clips is 7.88 seconds, with an average of 16 glosses per clip.

Quotes

"One primary objective of building the dataset is to investigate new modeling methods for how well large-vocabulary SLR/SLT can be done for a single signer, given a relatively large amount of his/her training data."
"Besides the TVB-HKSL-News dataset, we also introduce a data collection pipeline for SL data from TV programs, which was utilized during the collection of our dataset."

Key Insights Distilled From

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

by Zhe Niu,Rong... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00980.pdf

Deeper Inquiries

How can the automated data collection pipeline be further improved to scale up the collection of sign language data from various sources beyond TV programs?

The automated data collection pipeline can be enhanced in several ways to scale up the collection of sign language data from diverse sources beyond TV programs:

Incorporating Online Sources: The pipeline can be modified to scrape sign language videos from online platforms, educational websites, and social media channels where sign language content is shared.

Expanding Data Sources: By integrating APIs from sign language learning platforms, educational institutions, and sign language communities, the pipeline can access a wider range of sign language content for collection.

Implementing Language Detection: Including language detection algorithms in the pipeline can help identify sign language content in videos, enabling targeted collection of relevant data.

Enhancing OCR Techniques: Improving Optical Character Recognition (OCR) methods to accurately extract subtitles and text from videos in different languages and formats can enhance the pipeline's ability to collect data from a variety of sources.

Utilizing Crowdsourcing: Integrating crowdsourcing platforms to assist in data collection tasks, such as annotating sign language videos or transcribing sign glosses, can help scale up the pipeline's data acquisition process.

Collaborating with Sign Language Communities: Establishing partnerships with sign language organizations and communities to source authentic sign language content and ensure cultural sensitivity in data collection.

Implementing Quality Control Measures: Incorporating quality control mechanisms to verify the accuracy and relevance of collected data, such as human validation of annotations and data integrity checks, can improve the overall quality of the dataset.

How can the potential challenges in applying the signer-dependent modeling approach to real-world sign language recognition and translation systems be addressed?

Addressing the challenges in applying the signer-dependent modeling approach to real-world sign language recognition and translation systems involves:

Data Diversity: Ensuring the dataset used for training the models includes a diverse range of signers, signing styles, and linguistic variations to capture the complexity and variability of sign language expressions accurately.

Generalization: Developing models that can generalize well across different signers by incorporating techniques like transfer learning, domain adaptation, and multi-task learning to enhance performance on unseen signers.

Robustness to Variability: Enhancing the robustness of models to variations in signing speed, facial expressions, background noise, and environmental factors through data augmentation, regularization techniques, and robust feature representations.

Interpreting Co-articulations: Addressing the challenge of interpreting co-articulations and non-manual signals in sign language by incorporating multimodal features, such as facial expressions and body movements, into the modeling process.

Ethical Considerations: Ensuring ethical considerations in data collection, model development, and deployment to respect the privacy and cultural sensitivity of sign language users and communities.

User-Centric Design: Involving sign language users and experts in the design and evaluation of the models to ensure they meet the needs and preferences of the target user group.

Continuous Evaluation and Feedback: Implementing mechanisms for continuous evaluation, feedback collection, and model refinement based on user feedback and real-world performance metrics to iteratively improve the system.

How can the TVB-HKSL-News dataset be leveraged to develop more advanced sign language understanding models that go beyond just recognition and translation tasks?

The TVB-HKSL-News dataset can be leveraged to develop more advanced sign language understanding models by:

Sign Language Generation: Using the dataset to train models for sign language generation, enabling the automatic creation of sign language content based on textual input.

Sign Language Synthesis: Employing the dataset to develop sign language synthesis systems that can convert spoken language or text into sign language animations or avatars.

Sign Language Understanding: Enhancing models for sign language understanding tasks such as sentiment analysis, emotion recognition, and intent detection in sign language communication.

Sign Language Generation: Leveraging the dataset to create interactive sign language applications, virtual sign language tutors, and assistive technologies for sign language users.

Sign Language Annotation: Utilizing the dataset for sign language annotation tasks, sign language video summarization, and content indexing to facilitate sign language content retrieval and analysis.

Multimodal Integration: Integrating sign language data with other modalities like speech, text, and images to develop multimodal understanding systems for enhanced communication accessibility.

Real-time Sign Language Processing: Implementing real-time sign language recognition and translation systems for live interactions, video conferencing, and accessibility applications.

By exploring these advanced applications and research directions, the TVB-HKSL-News dataset can contribute to the development of innovative sign language technologies that cater to the diverse needs of the sign language community.