# Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction

arXiv: https://arxiv.org/abs/2404.12725 · HTML: https://mlsanity.com/paper/2404.12725/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [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)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation](https://mlsanity.com/paper/1804.03619/) (2018)
- [Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2306.17203/) (2023)
- [PMR: Prototypical Modal Rebalance for Multimodal Learning](https://mlsanity.com/paper/2211.07089/) (2022)
- [VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency](https://mlsanity.com/paper/2101.03149/) (2021)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks](https://mlsanity.com/paper/2202.05306/) (2022)
- [Robust Self-Supervised Audio-Visual Speech Recognition](https://mlsanity.com/paper/2201.01763/) (2022)
- [Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis](https://mlsanity.com/paper/2005.08209/) (2020)
- [Improving Multi-Modal Learning with Uni-Modal Teachers](https://mlsanity.com/paper/2106.11059/) (2021)
- [Lip to Speech Synthesis with Visual Context Attentional GAN](https://mlsanity.com/paper/2204.01726/) (2022)
- [SVTS: Scalable Video-to-Speech Synthesis](https://mlsanity.com/paper/2205.02058/) (2022)
- [SpEx+: A Complete Time Domain Speaker Extraction Network](https://mlsanity.com/paper/2005.04686/) (2020)
- [Time Domain Audio Visual Speech Separation](https://mlsanity.com/paper/1904.03760/) (2019)
- [SpEx: Multi-Scale Time Domain Speaker Extraction Network](https://mlsanity.com/paper/2004.08326/) (2020)
- [Intelligible Lip-to-Speech Synthesis with Speech Units](https://mlsanity.com/paper/2305.19603/) (2023)
- [DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding](https://mlsanity.com/paper/2308.07787/) (2023)
- [Muse: Multi-modal target speaker extraction with visual cues](https://mlsanity.com/paper/2010.07775/) (2020)
- [Lip-to-Speech Synthesis in the Wild with Multi-task Learning](https://mlsanity.com/paper/2302.08841/) (2023)
- [Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation](https://mlsanity.com/paper/2301.10752/) (2023)
- [AV-SepFormer: Cross-Attention SepFormer for Audio-Visual Target Speaker Extraction](https://mlsanity.com/paper/2306.14170/) (2023)
- [Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2307.02041/) (2023)
- [An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits](https://mlsanity.com/paper/2212.10744/) (2022)

## Built on by (most cited first, 7 of 7)

- [Cross-attention Inspired Selective State Space Models for Target Sound Extraction](https://mlsanity.com/paper/2409.04803/) (2024)
- [SepALM: Audio Language Models Are Error Correctors for Robust Speech Separation](https://mlsanity.com/paper/2505.03273/) (2025)
- [AV-CrossNet: an Audiovisual Complex Spectral Mapping Network for Speech Separation By Leveraging Narrow- and Cross-Band Modeling](https://mlsanity.com/paper/2406.11619/) (2024)
- [CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization](https://mlsanity.com/paper/2505.03186/) (2025)
- [Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction](https://mlsanity.com/paper/2506.09792/) (2025)
- [ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction](https://mlsanity.com/paper/2511.06288/) (2025)
- [Chunking Strategies for Multimodal AI Systems](https://mlsanity.com/paper/2512.00185/) (2025)

## Abstract (arXiv)

The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method.