Abstract
Sign language understanding has made significant strides; however, there is still no viable solution for generating sign sequences directlyfrom entire spoken content, e.g., text or speech. In this paper, we propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. In particular, a sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Moreover, by creating a joint embedding space for text, audio, and sign, we bind these modalities and leverage the semantic consistency among them to provide informative feedback for the model training. This embedding-consistency learning strategy minimizes the reliance on sign triplets and ensures continuous model refinement, evenwith a missing audio modality. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.- Anthology ID:
- 2024.findings-acl.432
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7241–7254
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.432
- DOI:
- 10.18653/v1/2024.findings-acl.432
- Cite (ACL):
- Jian Ma, Wenguan Wang, Yi Yang, and Feng Zheng. 2024. MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production. In Findings of the Association for Computational Linguistics: ACL 2024, pages 7241–7254, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production (Ma et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.findings-acl.432.pdf