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arXiv · 2505.22013

Overlap-Adaptive Hybrid Speaker Diarization and ASR-Aware Observation Addition for MISP 2025 Challenge

Shangkun Huang·Yuxuan Du·Jingwen Yang·Dejun Zhang·Xupeng Jia·Jing Deng·Jintao Kang·Rong Zheng
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Abstract

This paper presents the system developed to address the MISP 2025 Challenge. For the diarization system, we proposed a hybrid approach combining a WavLM end-to-end segmentation method with a traditional multi-module clustering technique to adaptively select the appropriate model for handling varying degrees of overlapping speech. For the automatic speech recognition (ASR) system, we proposed an ASR-aware observation addition method that compensates for the performance limitations of Guided Source Separation (GSS) under low signal-to-noise ratio conditions. Finally, we integrated the speaker diarization and ASR systems in a cascaded architecture to address Track 3. Our system achieved character error rates (CER) of 9.48% on Track 2 and concatenated minimum permutation character error rate (cpCER) of 11.56% on Track 3, ultimately securing first place in both tracks and thereby demonstrating the effectiveness of the proposed methods in real-world meeting scenarios.

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Shangkun Huang, Yuxuan Du, Jingwen Yang, Dejun Zhang, Xupeng Jia, Jing Deng, Jintao Kang, Rong Zheng. 2025-05-28. Overlap-Adaptive Hybrid Speaker Diarization and ASR-Aware Observation Addition for MISP 2025 Challenge. https://arxiv.org/abs/2505.22013

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