The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge

Abstract:This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice Question Answering (MCQA) across 21 languages. Three approaches are explored. First, we fine-tune Voxtral-Mini-3B via LoRA with cross-lingual data augmentation, ASR transcript augmentation and timestamp-aware audio cropping, achieving 0.72 macro-accuracy on evaluation Phase 2. Second, we apply multimodal in-context learning (ICL) to the frozen Voxtral-24B model to correct a strong label bias, reaching 0.81, our best result. Third, a training-free retrieval system based on a three-layer voice-anchored memory combining acoustic identity, semantic content, and a knowledge graph achieves 0.68. All three systems substantially outperform the official baseline.

Computation and Language (cs.CL)

arXiv:2609.11724 [cs.CL]

(or arXiv:2609.11724v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.11724 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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