Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings

Li Li; Ming Cheng; Juan Liu; Ming Li

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arXiv · 2510.09505

Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings

Li Li·Ming Cheng·Juan Liu·Ming Li

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Abstract

This paper proposes a Spatially-Augmented Sequence-to-Sequence Neural Diarization (SA-S2SND) framework, which integrates direction-of-arrival (DOA) cues estimated by SRP-DNN into the S2SND backbone. A two-stage training strategy is adopted: the model is first trained with single-channel audio and DOA features, and then further optimized with multi-channel inputs under DOA guidance. In addition, a simulated DOA generation scheme is introduced to alleviate dependence on matched multi-channel corpora. On the AliMeeting dataset, SA-S2SND consistently outperform the S2SND baseline, achieving a 7.4% relative DER reduction in the offline mode and over 19% improvement when combined with channel attention. These results demonstrate that spatial cues are highly complementary to cross-channel modeling, yielding good performance in both online and offline settings.

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BibTeX RIS

Li Li, Ming Cheng, Juan Liu, Ming Li. 2025-10-10. Spatially-Augmented Sequence-to-Sequence Neural Diarization for Meetings. https://arxiv.org/abs/2510.09505

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