Summary of Learning Co-speech Gesture Representations in Dialogue Through Contrastive Learning: An Intrinsic Evaluation, by Esam Ghaleb et al.
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation
by Esam Ghaleb, Bulat Khaertdinov, Wim Pouw, Marlou Rasenberg, Judith Holler, Aslı Özyürek, Raquel Fernández
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 This paper tackles the challenge of learning meaningful representations of co-speech gestures by employing self-supervised contrastive learning techniques. The authors propose an approach that includes both unimodal and multimodal pre-training to ground gesture representations in co-occurring speech. They use a face-to-face dialogue dataset rich with representational iconic gestures for training and conduct thorough intrinsic evaluations through comparison with human-annotated pairwise gesture similarity. The results show a significant positive correlation with human-annotated gesture similarity and reveal that the learned representations are consistent with well-motivated patterns related to the dynamics of dialogue interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning how to understand gestures we use when talking to each other. It’s tricky because gestures can mean different things depending on who’s speaking and what they’re saying. The researchers wanted to find a way to learn about these gestures by using a special kind of artificial intelligence training called contrastive learning. They used a big dataset of conversations with hand gestures and tested how well their method worked. They found that it was able to understand the similarities between different gestures, which is important for studying how we communicate. |
Keywords
» Artificial intelligence » Self supervised
