MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production

• URL: http://arxiv.org/abs/2407.12842v1
• Date: Thu, 4 Jul 2024 13:53:50 GMT
• Title: MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production
• Authors: Jian Ma, Wenguan Wang, Yi Yang, Feng Zheng,
• Abstract summary: We propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. A sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.
• Score: 93.32354378820648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sign language understanding has made significant strides; however, there is still no viable solution for generating sign sequences directly from 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, even with a missing audio modality. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.

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