Papers › Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal...
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive Systems
Dmitry Ryumin, Alexandr Axyonov, Elena Ryumina, Denis Ivanko, Alexey Kashevnik, Alexey Karpov
This article presents a research methodology for audio-visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio-visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on spatio-temporal fusion of audio and video feature matrices, (ii) a regulated transformer based on iterative model refinement module with multiple encoders, (iii) a classifier ensemble strategy based on multiple decoders. The spatio-temporal fusion strategy preserves contextual information of both modalities and achieves their synchronization. An iterative model refinement module can bridge the gap between acoustic and visual data by leveraging their impact on speech recognition accuracy. The proposed multi-prediction strategy demonstrates superior performance compared to traditional single-prediction strategy, showcasing the model’s adaptability across diverse audio-visual contexts. The transformer proposed has achieved the highest values of speech command recognition accuracy, reaching 98.87% and 98.81% on the RUSAVIC and LRW corpora, respectively. This research has significant implications for advancing human–computer interaction. The capabilities of AVCRFormer extend beyond AVSR, making it a valuable contribution to the intersection of audio-visual processing and artificial intelligence.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio-Visual Speech Recognition | LRW | AVCRFormer | Top-1 Accuracy | 98.81 | #1 of 3 | Archive leaderboard | report |
| Lipreading | Lip Reading in the Wild | AVCRFormer | Top-1 Accuracy | 89.57 | #4 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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