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arXiv · 2606.29497

Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR

Yichi Wang·Junzhe Chen·Wangjin Zhou·Tatsuya Kawahara
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Abstract

In long-form multi-party conversations, highly imbalanced speaker activity and frequent overlap make it difficult to identify "who spoke when and what". Sliding-window continuous speech separation (CSS) mitigates sparse supervision, but often suffers from cross-window speaker inconsistency and residual crosstalk, which in practice requires diarization for reliable speaker attribution. Motivated by the stability of speakers' directions of arrival (DOAs) in meetings, we propose PATSE, a multi-channel Position-Aware Target Speaker Extraction front-end that uses DOA as a spatial prior to directly extract the speech of each target speaker. PATSE combines a DOA-guided spatial encoder and conditioner to generate speaker-attributed streams, from which speaker activity can be inferred via simple post-processing (e.g., VAD) without explicit diarization. Experiments on both replayed and real conversations show consistent ASR gains outperforming CSS and diarization-based pipelines.

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Yichi Wang, Junzhe Chen, Wangjin Zhou, Tatsuya Kawahara. 2026-06-28. Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR. https://arxiv.org/abs/2606.29497

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