Transsion's Speaker-Attributed Multilingual ASR System for the MLC-SLM 2026 Challenge

Abstract:This paper presents the Transsion Speech Team submission to Task 1 of the MLC-SLM 2026 Challenge, which focuses on speaker-attributed transcription for multilingual conversational speech. We propose a cascaded framework consisting of three components: a speaker diarization module, a long-form multilingual ASR module, and a speaker-transcription fusion module. The diarization module is built upon DiariZen and produces speaker-homogeneous segments through local speaker activity estimation and global speaker clustering. The ASR module is based on Qwen3-Omni and generates multilingual transcriptions, while an external CTC-based alignment model provides precise word- and character-level timestamps. Finally, the fusion module combines diarization outputs with timestamped transcriptions to generate speaker-attributed STM outputs. Experimental results on the official evaluation set demonstrate the effectiveness of the proposed framework. The submitted system achieves a tcpMER of 15.41% and ranks second among all participating teams.

Computation and Language (cs.CL)

arXiv:2609.20833 [cs.CL]

(or arXiv:2609.20833v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.20833 Focus to learn more arXiv-issued DOI via DataCite

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