# TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning

arXiv: https://arxiv.org/abs/2309.12306 · HTML: https://mlsanity.com/paper/2309.12306/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](https://mlsanity.com/paper/1804.03641/) (2018)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection](https://mlsanity.com/paper/2107.06592/) (2021)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](https://mlsanity.com/paper/1901.01342/) (2019)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Active Speakers in Context](https://mlsanity.com/paper/2005.09812/) (2020)
- [A Light Weight Model for Active Speaker Detection](https://mlsanity.com/paper/2303.04439/) (2023)
- [MAAS: Multi-modal Assignation for Active Speaker Detection](https://mlsanity.com/paper/2101.03682/) (2021)
- [How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild](https://mlsanity.com/paper/2106.03932/) (2021)
- [Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection](https://mlsanity.com/paper/2207.07783/) (2022)
- [End-to-End Active Speaker Detection](https://mlsanity.com/paper/2203.14250/) (2022)
- [LoCoNet: Long-Short Context Network for Active Speaker Detection](https://mlsanity.com/paper/2301.08237/) (2023)
- [Who said that?: Audio-visual speaker diarisation of real-world meetings](https://mlsanity.com/paper/1906.10042/) (2019)
- [Look Who's Talking: Active Speaker Detection in the Wild](https://mlsanity.com/paper/2108.07640/) (2021)
- [Target Active Speaker Detection with Audio-visual Cues](https://mlsanity.com/paper/2305.12831/) (2023)

## Built on by (most cited first, 16 of 16)

- [FlowAVSE: Efficient Audio-Visual Speech Enhancement with Conditional Flow Matching](https://mlsanity.com/paper/2406.09286/) (2024)
- [See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models](https://mlsanity.com/paper/2512.02231/) (2025)
- [Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies](https://mlsanity.com/paper/2410.21170/) (2024)
- [LASER: Lip Landmark Assisted Speaker Detection for Robustness](https://mlsanity.com/paper/2501.11899/) (2025)
- [Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness](https://mlsanity.com/paper/2505.21954/) (2025)
- [MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild](https://mlsanity.com/paper/2606.16731/) (2026)
- [Imitation of human motion achieves natural head movements for humanoid robots in an active-speaker detection task](https://mlsanity.com/paper/2407.11915/) (2024)
- [An Efficient and Streaming Audio Visual Active Speaker Detection System](https://mlsanity.com/paper/2409.09018/) (2024)
- [GateFusion: Hierarchical Gated Cross-Modal Fusion for Active Speaker Detection](https://mlsanity.com/paper/2512.15707/) (2025)
- [Who Says What: Symbolic Trimodal Binding Mechanisms in Audio-Visual LLMs](https://mlsanity.com/paper/2609.31193/) (2026)
- [FabuLight-ASD: Unveiling Speech Activity via Body Language](https://mlsanity.com/paper/2411.13674/) (2024)
- [C3ASD: Multi-Level Consistency-Driven Representation Learning for Robust Active Speaker Detection](https://mlsanity.com/paper/2607.03018/) (2026)
- [Talking to Me or Someone Else? Rethinking Talk-to-Me Detection in Egocentric Videos](https://mlsanity.com/paper/2609.14118/) (2026)
- [ROAM-ASD: Robust Open-World Active Speaker Detection with Flexible Multimodal Fusion](https://mlsanity.com/paper/2609.26648/) (2026)
- [EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold](https://mlsanity.com/paper/2609.35616/) (2026)
- [CAST-ASD: Causal Attention over Speakers and Time for Online Active Speaker Detection](https://mlsanity.com/paper/2610.14676/) (2026)

## Abstract (arXiv)

The goal of this work is Active Speaker Detection (ASD), a task to determine whether a person is speaking or not in a series of video frames. Previous works have dealt with the task by exploring network architectures while learning effective representations has been less explored. In this work, we propose TalkNCE, a novel talk-aware contrastive loss. The loss is only applied to part of the full segments where a person on the screen is actually speaking. This encourages the model to learn effective representations through the natural correspondence of speech and facial movements. Our loss can be jointly optimized with the existing objectives for training ASD models without the need for additional supervision or training data. The experiments demonstrate that our loss can be easily integrated into the existing ASD frameworks, improving their performance. Our method achieves state-of-the-art performances on AVA-ActiveSpeaker and ASW datasets.