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Attention-Guided Reliability Scaling for Contrastive Decoding in Robust Audio-Visual Speech Recognition

2026-08-26 08:25:15
YoungChae Kim, Da-Hee Yang, Joon-Hyuk Chang
arXiv_CV
                   
Abstract

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. Instead of using a fixed strength, we adaptively modulate the contrastive influence at each token based on reliability signals derived from attention dynamics and inter-model predictive divergence. Experiments on LRS3 show consistent improvements across clean and low-SNR conditions.

Abstract (translated)

URL

https://arxiv.org/abs/2608.26213

PDF

https://arxiv.org/pdf/2608.26213.pdf

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