# Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture

arXiv: https://arxiv.org/abs/1810.00108 · HTML: https://mlsanity.com/paper/1810.00108/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 30 of 64)

- [End-to-end Audio-visual Speech Recognition with Conformers](https://mlsanity.com/paper/2102.06657/) (2021)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels](https://mlsanity.com/paper/2303.14307/) (2023)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [Sub-word Level Lip Reading With Visual Attention](https://mlsanity.com/paper/2110.07603/) (2021)
- [Jointly Learning Visual and Auditory Speech Representations from Raw Data](https://mlsanity.com/paper/2212.06246/) (2022)
- [LiRA: Learning Visual Speech Representations from Audio through Self-supervision](https://mlsanity.com/paper/2106.09171/) (2021)
- [Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring](https://mlsanity.com/paper/2303.08536/) (2023)
- [Lip to Speech Synthesis with Visual Context Attentional GAN](https://mlsanity.com/paper/2204.01726/) (2022)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition](https://mlsanity.com/paper/2207.06020/) (2022)
- [Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning](https://mlsanity.com/paper/2212.05301/) (2022)
- [Audio-Visual Efficient Conformer for Robust Speech Recognition](https://mlsanity.com/paper/2301.01456/) (2023)
- [MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens](https://mlsanity.com/paper/2503.11315/) (2025)
- [Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition](https://mlsanity.com/paper/2203.07996/) (2022)
- [Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping](https://mlsanity.com/paper/2308.06112/) (2023)
- [Visual Keyword Spotting with Attention](https://mlsanity.com/paper/2110.15957/) (2021)
- [Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs](https://mlsanity.com/paper/2503.06362/) (2025)
- [VALLR: Visual ASR Language Model for Lip Reading](https://mlsanity.com/paper/2503.21408/) (2025)
- [Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach](https://mlsanity.com/paper/2505.14336/) (2025)
- [Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation](https://mlsanity.com/paper/2212.02782/) (2022)
- [Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2305.09212/) (2023)
- [Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations](https://mlsanity.com/paper/2507.03304/) (2025)
- [OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment](https://mlsanity.com/paper/2306.06410/) (2023)
- [Efficient Training for Multilingual Visual Speech Recognition: Pre-training with Discretized Visual Speech Representation](https://mlsanity.com/paper/2401.09802/) (2024)
- [SimulLR: Simultaneous Lip Reading Transducer with Attention-Guided Adaptive Memory](https://mlsanity.com/paper/2108.13630/) (2021)
- [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)
- [AlignDiT: Multimodal Aligned Diffusion Transformer for Synchronized Speech Generation](https://mlsanity.com/paper/2504.20629/) (2025)
- [Phoneme-Level Visual Speech Recognition via Point-Visual Fusion and Language Model Reconstruction](https://mlsanity.com/paper/2507.18863/) (2025)

## Abstract (arXiv)

Recent works in speech recognition rely either on connectionist temporal classification (CTC) or sequence-to-sequence models for character-level recognition. CTC assumes conditional independence of individual characters, whereas attention-based models can provide nonsequential alignments. Therefore, we could use a CTC loss in combination with an attention-based model in order to force monotonic alignments and at the same time get rid of the conditional independence assumption. In this paper, we use the recently proposed hybrid CTC/attention architecture for audio-visual recognition of speech in-the-wild. To the best of our knowledge, this is the first time that such a hybrid architecture architecture is used for audio-visual recognition of speech. We use the LRS2 database and show that the proposed audio-visual model leads to an 1.3% absolute decrease in word error rate over the audio-only model and achieves the new state-of-the-art performance on LRS2 database (7% word error rate). We also observe that the audio-visual model significantly outperforms the audio-based model (up to 32.9% absolute improvement in word error rate) for several different types of noise as the signal-to-noise ratio decreases.