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arXiv · 2607.12468

An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge

Shuming Fang·Shuifei Zeng
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Abstract

We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time. On the official Development set (150 conversations, 21 language/accent categories) the system attains a macro tcpMER of 29.27%, versus 79.15% for the official baseline; on the Evaluation set it scores 50.23%. We also analyze two engineering choices that substantially affect tcpMER. First, embedding-based speaker clustering outperforms an end-to-end-style alternative that assigns speakers from ASR turn markers alone. Second, overlap-aware segmentation, although intended to raise diarization recall, increases tcpMER because overlapped speech is transcribed twice.

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Shuming Fang, Shuifei Zeng. 2026-07-14. An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge. https://arxiv.org/abs/2607.12468

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