Papers › Visual Speech Recognition in a Driver Assistance System
Visual Speech Recognition in a Driver Assistance System
Denis Ivanko, Dmitry Ryumin, Alexey Kashevnik, Alexandr Axyonov, Alexey Karpov
Visual speech recognition or automated lipreading is a field of growing attention. Video data proved its usefulness in multimodal speech recognition, especially when acoustic data is heavily noised or even inaccessible. In this paper, we present a novel method for visual speech recognition. We benchmark it on the famous LRW lip-reading dataset by outperforming the existing approaches. After a comprehensive evaluation, we adapt the developed method and test it on the collected RUSAVIC corpus we recorded in-the-wild for vehicle driver. The results obtained demonstrate not only the high performance of the proposed method, but also the fundamental possibility of recognizing speech only by using video modality, even in such difficult natural conditions as driving.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lipreading | Lip Reading in the Wild | Vosk + MediaPipe + LS + MixUp + SA + 3DResNet-18 + BiLSTM + Cosine WR | Top-1 Accuracy | 88.7 | #6 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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