Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness

Le Thien Phuc Nguyen; Zhuoran Yu; Khoa Quang Nhat Cao; Yuwei Guo; Tu Ho Manh Pham; Tuan Tai Nguyen; Toan Ngo Duc Vo; Lucas Poon; Tuan Khai Nguyen; Soochahn Lee; Yong Jae Lee

arXiv · 2505.21954

Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness

Le Thien Phuc Nguyen·Zhuoran Yu·Khoa Quang Nhat Cao·Yuwei Guo·Tu Ho Manh Pham·Tuan Tai Nguyen·Toan Ngo Duc Vo·Lucas Poon·Tuan Khai Nguyen·Soochahn Lee·Yong Jae Lee

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Abstract

We present UniTalk, a novel dataset emphasizing challenging scenarios to enhance model generalization for the task of active speaker detection (ASD). Previously established benchmarks such as AVA predominantly comprise old movies and thus exhibit significant domain gaps with real-world video. In contrast, UniTalk covers diverse video types reflecting challenging real-world conditions, including underrepresented languages, noisy backgrounds, and crowded scenes, while being on par with AVA in scale. Extensive evaluations reveal that ASD remains unsolved under realistic conditions: state-of-the-art models near-perfect on AVA fail to reach saturation on UniTalk. Conversely, models trained on UniTalk generalize better to modern in-the-wild datasets including Talkies and ASW. UniTalk thus establishes a new benchmark for ASD, providing researchers with a valuable resource for developing and evaluating versatile and resilient models.

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Le Thien Phuc Nguyen, Zhuoran Yu, Khoa Quang Nhat Cao, Yuwei Guo, Tu Ho Manh Pham, Tuan Tai Nguyen, Toan Ngo Duc Vo, Lucas Poon, Tuan Khai Nguyen, Soochahn Lee, Yong Jae Lee. 2026-06-17. Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness. https://arxiv.org/abs/2505.21954

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