Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness
arXiv:2505.21954v2 Announce Type: replace-cross 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 r
The proliferation of real-world video data and the increasing demand for robust AI models drive the need for more representative benchmarks in computer vision.
Improved active speaker detection in challenging, real-world conditions is crucial for the development of more generalizable and robust AI systems in various applications.
The introduction of UniTalk provides a more rigorous benchmark for evaluating active speaker detection models, pushing the field towards greater real-world applicability.
- · AI researchers
- · Computer Vision developers
- · Video analytics companies
- · AI applications requiring robust audio-visual analysis
- · Models overfitting on older, less diverse datasets
- · Entities relying solely on synthetic or clean data for model training
Active speaker detection models will improve in their ability to perform in diverse, noisy real-world environments.
Better active speaker detection can enhance multimodal AI systems, leading to more natural human-computer interaction and improved surveillance.
The ability to accurately identify active speakers in complex settings could enable new forms of automated interview analysis, meeting summarization, and content indexing.
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Read at arXiv cs.AI