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    <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.</abstract>
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%0 Conference Proceedings
%T MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production
%A Ma, Jian
%A Wang, Wenguan
%A Yang, Yi
%A Zheng, Feng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F ma-etal-2024-ms2sl
%X 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.
%U https://aclanthology.org/2024.findings-acl.432
%P 7241-7254

Markdown (Informal)

[MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production](https://aclanthology.org/2024.findings-acl.432) (Ma et al., Findings 2024)

• MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production (Ma et al., Findings 2024)

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 and virtual meeting. Association for Computational Linguistics.