# u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality

arXiv: https://arxiv.org/abs/2207.07036 · HTML: https://mlsanity.com/paper/2207.07036/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 69 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [Omnivore: A Single Model for Many Visual Modalities](https://mlsanity.com/paper/2201.08377/) (2022)
- [UniT: Multimodal Multitask Learning with a Unified Transformer](https://mlsanity.com/paper/2102.10772/) (2021)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [Transformers without Tears: Improving the Normalization of Self-Attention](https://mlsanity.com/paper/1910.05895/) (2019)

## Built on by (most cited first, 30 of 39)

- [Self-Supervised Multimodal Learning: A Survey](https://mlsanity.com/paper/2304.01008/) (2023)
- [Large Language Models are Strong Audio-Visual Speech Recognition Learners](https://mlsanity.com/paper/2409.12319/) (2024)
- [MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation](https://mlsanity.com/paper/2303.00628/) (2023)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Toward Joint Language Modeling for Speech Units and Text](https://mlsanity.com/paper/2310.08715/) (2023)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [BRAVEn: Improving Self-Supervised Pre-training for Visual and Auditory Speech Recognition](https://mlsanity.com/paper/2404.02098/) (2024)
- [Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs](https://mlsanity.com/paper/2411.02256/) (2024)
- [AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation](https://mlsanity.com/paper/2312.02512/) (2023)
- [LANISTR: Multimodal Learning from Structured and Unstructured Data](https://mlsanity.com/paper/2305.16556/) (2023)
- [XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception](https://mlsanity.com/paper/2403.14402/) (2024)
- [Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs](https://mlsanity.com/paper/2503.06362/) (2025)
- [Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach](https://mlsanity.com/paper/2505.14336/) (2025)
- [Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2305.09212/) (2023)
- [Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations](https://mlsanity.com/paper/2503.06273/) (2025)
- [From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech](https://mlsanity.com/paper/2503.16956/) (2025)
- [ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement](https://mlsanity.com/paper/2212.11377/) (2022)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)
- [Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer](https://mlsanity.com/paper/2405.12983/) (2024)
- [Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2407.03563/) (2024)
- [Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout](https://mlsanity.com/paper/2409.07078/) (2024)
- [AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2309.17395/) (2023)
- [Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation](https://mlsanity.com/paper/2504.18539/) (2025)
- [MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10567/) (2023)
- [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)
- [Omni-AVSR: Towards Unified Multimodal Speech Recognition with Large Language Models](https://mlsanity.com/paper/2511.07253/) (2025)
- [Audio-visual fine-tuning of audio-only ASR models](https://mlsanity.com/paper/2312.09369/) (2023)
- [Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio](https://mlsanity.com/paper/2310.10922/) (2023)

## Abstract (arXiv)

While audio-visual speech models can yield superior performance and robustness compared to audio-only models, their development and adoption are hindered by the lack of labeled and unlabeled audio-visual data and the cost to deploy one model per modality. In this paper, we present u-HuBERT, a self-supervised pre-training framework that can leverage both multimodal and unimodal speech with a unified masked cluster prediction objective. By utilizing modality dropout during pre-training, we demonstrate that a single fine-tuned model can achieve performance on par or better than the state-of-the-art modality-specific models. Moreover, our model fine-tuned only on audio can perform well with audio-visual and visual speech input, achieving zero-shot modality generalization for multiple speech processing tasks. In particular, our single model yields 1.2%/1.4%/27.2% speech recognition word error rate on LRS3 with audio-visual/audio/visual input. Codes and models are available at https://github.com/facebookresearch/av_hubert