EEND-SAA: Enrollment-Less Main Speaker Voice Activity Detection Using Self-Attention Attractors

Wen-Yung Wu; Pei-Chin Hsieh; Tai-Shih Chi

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arXiv · 2509.11957

EEND-SAA: Enrollment-Less Main Speaker Voice Activity Detection Using Self-Attention Attractors

Wen-Yung Wu·Pei-Chin Hsieh·Tai-Shih Chi

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Abstract

Voice activity detection (VAD) is essential in speech-based systems, but traditional methods detect only speech presence without identifying speakers. Target-speaker VAD (TS-VAD) extends this by detecting the speech of a known speaker using a short enrollment utterance, but this assumption fails in open-domain scenarios such as meetings or customer service calls, where the main speaker is unknown. We propose EEND-SAA, an enrollment-less, streaming-compatible framework for main-speaker VAD, which identifies the primary speaker without prior knowledge. Unlike TS-VAD, our method determines the main speaker as the one who talks more steadily and clearly, based on speech continuity and volume. We build our model on EEND using two self-attention attractors in a Transformer and apply causal masking for real-time use. Experiments on multi-speaker LibriSpeech mixtures show that EEND-SAA reduces main-speaker DER from 6.63% to 3.61% and improves F1 from 0.9667 to 0.9818 over the SA-EEND baseline, achieving state-of-the-art performance under conditions involving speaker overlap and noise.

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BibTeX RIS

Wen-Yung Wu, Pei-Chin Hsieh, Tai-Shih Chi. 2025-09-15. EEND-SAA: Enrollment-Less Main Speaker Voice Activity Detection Using Self-Attention Attractors. https://arxiv.org/abs/2509.11957

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