Attention-Guided Reliability Scaling for Contrastive Decoding in Robust Audio-Visual Speech Recognition
Large language model (LLM)-based audio-visual speech recognition (AVSR) systems are robust under noise. Contrastive decoding (CD), originally introduced to stabilize LLM generation by contrasting a weaker model against a stronger one at inference time, adjusts predictions without additional training. In this work, we apply CD to AVSR by contrasting audio-only conditioning with full audio-visual conditioning within the same underlying model. However, using a fixed contrastive strength introduces a trade-off across noise levels: stronger intervention helps under severe noise but may over-correct reliable predictions in clean conditions. We propose reliability-aware scaling of CD for AVSR. I...
| Authors | YoungChae Kim, Da-Hee Yang, Joon-Hyuk Chang |
|---|---|
| Primary category | cs.SD |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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