# XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception

arXiv: https://arxiv.org/abs/2403.14402 · HTML: https://mlsanity.com/paper/2403.14402/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [Self-supervised Learning with Random-projection Quantizer for Speech Recognition](https://mlsanity.com/paper/2202.01855/) (2022)
- [End-to-end Audio-visual Speech Recognition with Conformers](https://mlsanity.com/paper/2102.06657/) (2021)
- [Lipreading using Temporal Convolutional Networks](https://mlsanity.com/paper/2001.08702/) (2020)
- [Combining Residual Networks with LSTMs for Lipreading](https://mlsanity.com/paper/1703.04105/) (2017)
- [Jointly Learning Visual and Auditory Speech Representations from Raw Data](https://mlsanity.com/paper/2212.06246/) (2022)
- [u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality](https://mlsanity.com/paper/2207.07036/) (2022)
- [MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation](https://mlsanity.com/paper/2303.00628/) (2023)
- [Deep Multimodal Learning for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1501.05396/) (2015)
- [VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning](https://mlsanity.com/paper/2211.11275/) (2022)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning](https://mlsanity.com/paper/2210.15324/) (2022)
- [AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR](https://mlsanity.com/paper/2303.16501/) (2023)
- [MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition](https://mlsanity.com/paper/2303.05309/) (2023)
- [AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation](https://mlsanity.com/paper/2312.02512/) (2023)
- [Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation](https://mlsanity.com/paper/2212.02782/) (2022)
- [Audio-visual fine-tuning of audio-only ASR models](https://mlsanity.com/paper/2312.09369/) (2023)

## Built on by (most cited first, 11 of 11)

- [SpeechQE: Estimating the Quality of Direct Speech Translation](https://mlsanity.com/paper/2410.21485/) (2024)
- [Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations](https://mlsanity.com/paper/2503.06273/) (2025)
- [mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition](https://mlsanity.com/paper/2502.01547/) (2025)
- [MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition](https://mlsanity.com/paper/2502.10447/) (2025)
- [MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2510.04136/) (2025)
- [Tailored Design of Audio-Visual Speech Recognition Models using Branchformers](https://mlsanity.com/paper/2407.06606/) (2024)
- [AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR](https://mlsanity.com/paper/2501.07102/) (2025)
- [CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization](https://mlsanity.com/paper/2505.03186/) (2025)
- [Leveraging Audio-Visual Data to Reduce the Multilingual Gap in Self-Supervised Speech Models](https://mlsanity.com/paper/2509.17523/) (2025)
- [Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses](https://mlsanity.com/paper/2510.13281/) (2025)
- [Measuring User's Mental Models of Speech Translation in Human-AI Collaboration](https://mlsanity.com/paper/2606.24644/) (2026)

## Abstract (arXiv)

Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. Augmenting these systems with visual signals has the potential to improve robustness to noise. However, audio-visual (AV) data is only available in limited amounts and for fewer languages than audio-only resources. To address this gap, we present XLAVS-R, a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages. It is designed to maximize the benefits of limited multilingual AV pre-training data, by building on top of audio-only multilingual pre-training and simplifying existing pre-training schemes. Extensive evaluation on the MuAViC benchmark shows the strength of XLAVS-R on downstream audio-visual speech recognition and translation tasks, where it outperforms the previous state of the art by up to 18.5% WER and 4.7 BLEU given noisy AV inputs, and enables strong zero-shot audio-visual ability with audio-only fine-tuning.