Eloquence improves multilingual audio quiz answering accuracy
The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge
Computation and Language
Summary
Answering multiple-choice questions from audio in many languages is hard because of differences in speech and text. The Eloquence team tried three ways to solve this: changing a smaller model with added language variation and audio tweaks, using a big fixed model to fix mistakes by looking at examples, and a system that remembers sounds, meanings, and facts without training. Their best method reached 81% accuracy, beating the existing baseline by a lot.
What this means in practice
- •For multilingual voice assistant developers: Improve accuracy of question answering from user audio queries in many languages by using nuanced audio and text data enhancements.
- •For call center automation teams: Enhance multilingual automated responses by integrating a fast retrieval memory system combining voice identity and content without retraining models.
Authors
Jordi Luque, Lorenzo Concina, Marco Matassoni, Alessio Brutti, Filippo Vella
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.