Summary of Ms2sl: Multimodal Spoken Data-driven Continuous Sign Language Production, by Jian Ma et al.
MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production
by Jian Ma, Wenguan Wang, Yi Yang, Feng Zheng
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed unified framework for continuous sign language production enables seamless communication between sign and non-sign language users by generating sign sequences directly from entire spoken content, such as text or speech. A sequence diffusion model is developed to predict signs step-by-step using embeddings extracted from text or speech. The framework also includes an embedding-consistency learning strategy that leverages semantic consistency among modalities (text, audio, and sign) to provide informative feedback for model training. This approach minimizes the reliance on sign triplets and ensures continuous model refinement, even with a missing audio modality. Experimental results on How2Sign and PHOENIX14T datasets demonstrate competitive performance in sign language production. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help people who use sign language communicate better with those who don’t is being developed. This method can turn spoken words into sign language, step by step. It uses special computer code to understand the meaning of text or speech and create a sequence of signs that match what’s being said. The system also makes sure it’s learning from all the different ways we communicate (text, audio, and sign) to get better at its job. This means it can still improve even if some parts of the communication are missing. Tests show that this method works well for creating sign language sequences. |
Keywords
» Artificial intelligence » Diffusion model » Embedding
