Summary of Syncvsr: Data-efficient Visual Speech Recognition with End-to-end Crossmodal Audio Token Synchronization, by Young Jin Ahn and Jungwoo Park and Sangha Park and Jonghyun Choi and Kee-eung Kim
SyncVSR: Data-Efficient Visual Speech Recognition with End-to-End Crossmodal Audio Token Synchronization
by Young Jin Ahn, Jungwoo Park, Sangha Park, Jonghyun Choi, Kee-Eung Kim
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 presents SyncVSR, an end-to-end learning framework for Visual Speech Recognition (VSR) that tackles the challenge of distinguishing homophenes in spoken content from visual cues. The framework leverages quantized audio for frame-level crossmodal supervision and integrates a projection layer to synchronize visual representation with acoustic data. This allows the encoder to generate discrete audio tokens from a video sequence in a non-autoregressive manner. SyncVSR achieves state-of-the-art results across tasks, languages, and modalities at the cost of a single forward pass, while reducing data usage by up to ninefold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SyncVSR is a new way to recognize spoken words based on what people look like when they say them. Right now, it’s hard to tell apart similar lip movements that mean different things. To fix this, the researchers created a special kind of AI that uses sound and video together to figure out what someone is saying. This helps it do better than other systems that just use one or the other. The new system works well across different languages and types of tasks, and it even uses less data than before. |
Keywords
» Artificial intelligence » Autoregressive » Encoder
