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SEAR system achieves 90.92% accuracy in multilingual speech challenge

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a system called SEAR for the Multilingual Conversational Speech Language Model (MLC-SLM) Challenge, achieving 90.92% accuracy. The system adapts the Qwen3-Omni-30B-A3B-Instruct model by converting ASR transcripts into event spans, which are then used to synthesize semantic and acoustic multiple-choice questions. These questions are generated using Qwen3.6-27B for semantic MCQs and Gemini 3.1 Flash-Lite for acoustic MCQs, with rigorous verification steps ensuring data quality. AI

IMPACT This research advances techniques for multilingual speech understanding and data synthesis for language models.

RANK_REASON The item is a research paper detailing a system for a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SEAR system achieves 90.92% accuracy in multilingual speech challenge

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The item is a research paper detailing a system for a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Huy Hoang Le, Long-Bao Nguyen, Minh Tri Dao ·

    SEAR: Segment-Evidence-Aware Routing for Weak-to-Strong Multilingual Speech MCQ

    arXiv:2609.11355v1 Announce Type: new Abstract: This paper describes our system for Task~2 of the second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge. We adapt Qwen3-Omni-30B-A3B-Instruct with a segment-evidence-aware data and post-training pipeline. A la…

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