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arXiv · 2505.23207

Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM

Zhaokai Sun·Li Zhang·Qing Wang·Pan Zhou·Lei Xie
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Abstract

Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation, a critical challenge in multi-party speech processing. This work proposes a speaker-aware progressive OSD model that leverages a progressive training strategy to enhance the correlation between subtasks such as voice activity detection (VAD) and overlap detection. To improve acoustic representation, we explore the effectiveness of state-of-the-art self-supervised learning (SSL) models, including WavLM and wav2vec 2.0, while incorporating a speaker attention module to enrich features with frame-level speaker information. Experimental results show that the proposed method achieves state-of-the-art performance, with an F1 score of 82.76\% on the AMI test set, demonstrating its robustness and effectiveness in OSD.

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BibTeXRIS

Zhaokai Sun, Li Zhang, Qing Wang, Pan Zhou, Lei Xie. 2025-05-29. Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM. https://arxiv.org/abs/2505.23207

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