A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision

• URL: http://arxiv.org/abs/2405.10266v1
• Date: Thu, 16 May 2024 17:19:06 GMT
• Title: A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision
• Authors: Charles Raude, K R Prajwal, Liliane Momeni, Hannah Bull, Samuel Albanie, Andrew Zisserman, Gül Varol,
• Abstract summary: We introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text. New dataset annotations provide continuous sign-level annotations for six hours of test videos, and will be made publicly available. Our model significantly outperforms the previous state of the art on both tasks.
• Score: 74.972172804514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text. To enable CSLR evaluation in the large-vocabulary setting, we introduce new dataset annotations that have been manually collected. These provide continuous sign-level annotations for six hours of test videos, and will be made publicly available. We demonstrate that by a careful choice of loss functions, training the model for both the CSLR and retrieval tasks is mutually beneficial in terms of performance -- retrieval improves CSLR performance by providing context, while CSLR improves retrieval with more fine-grained supervision. We further show the benefits of leveraging weak and noisy supervision from large-vocabulary datasets such as BOBSL, namely sign-level pseudo-labels, and English subtitles. Our model significantly outperforms the previous state of the art on both tasks.

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