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

• URL: http://arxiv.org/abs/2505.23207v1
• Date: Thu, 29 May 2025 07:47:48 GMT
• Title: Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM
• Authors: Zhaokai Sun, Li Zhang, Qing Wang, Pan Zhou, Lei Xie,
• Abstract summary: Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation.<n>This work proposes a speaker-aware progressive OSD model that leverages a progressive training strategy to enhance the correlation between subtasks.<n> 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.
• Score: 53.17360668423001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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