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arXiv · 2603.25377

Joint Learning Global-Local Speaker Classification to Enhance End-to-End Speaker Diarization and Recognition

Yuhang Dai·Haopeng Lin·Jiale Qian·Ruiqi Yan·Hao Meng·Hanke Xie·Hanlin Wen·Shunshun Yin·Ming Tao·Xie Chen·Lei Xie·Xinsheng Wang
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Abstract

Large Audio-Language Models (LALMs) have demonstrated remarkable performance in end-to-end speaker diarization and recognition. However, their speaker discriminability remains limited due to the scarcity of large-scale conversational data and the absence of explicit speaker representation optimization. To address this, we propose GLSC-SDR, a paradigm that jointly trains speaker classification with diarization and recognition. We further introduce a Global-Local Speaker Classification strategy, which uses clustered speakers as global labels and re-encoded intra-cluster speakers as local labels. This hierarchical design enhances fine-grained speaker discrimination while preserving semantic transcription accuracy. Experiments on AliMeeting, AISHELL-4, and AMI-SDM demonstrate that GLSC-SDR achieves competitive or superior performance compared to simulation-based and multi-encoder approaches, without relying on large-scale real conversational data.

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Yuhang Dai, Haopeng Lin, Jiale Qian, Ruiqi Yan, Hao Meng, Hanke Xie, Hanlin Wen, Shunshun Yin, Ming Tao, Xie Chen, Lei Xie, Xinsheng Wang. 2026-03-26. Joint Learning Global-Local Speaker Classification to Enhance End-to-End Speaker Diarization and Recognition. https://arxiv.org/abs/2603.25377

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