# Looking Enhances Listening: Recovering Missing Speech Using Images

arXiv: https://arxiv.org/abs/2002.05639 · HTML: https://mlsanity.com/paper/2002.05639/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR](https://mlsanity.com/paper/2303.16501/) (2023)
- [AVATAR: Unconstrained Audiovisual Speech Recognition](https://mlsanity.com/paper/2206.07684/) (2022)
- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [Multimodal Speech Recognition for Language-Guided Embodied Agents](https://mlsanity.com/paper/2302.14030/) (2023)
- [VHASR: A Multimodal Speech Recognition System With Vision Hotwords](https://mlsanity.com/paper/2410.00822/) (2024)

## Abstract (arXiv)

Speech is understood better by using visual context; for this reason, there have been many attempts to use images to adapt automatic speech recognition (ASR) systems. Current work, however, has shown that visually adapted ASR models only use images as a regularization signal, while completely ignoring their semantic content. In this paper, we present a set of experiments where we show the utility of the visual modality under noisy conditions. Our results show that multimodal ASR models can recover words which are masked in the input acoustic signal, by grounding its transcriptions using the visual representations. We observe that integrating visual context can result in up to 35% relative improvement in masked word recovery. These results demonstrate that end-to-end multimodal ASR systems can become more robust to noise by leveraging the visual context.