# The Eloquence team submission for task 1 of MLC-SLM challenge

arXiv: https://arxiv.org/abs/2507.19308 · HTML: https://mlsanity.com/paper/2507.19308/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [EuroLLM: Multilingual Language Models for Europe](https://mlsanity.com/paper/2409.16235/) (2024)
- [An Embarrassingly Simple Approach for LLM with Strong ASR Capacity](https://mlsanity.com/paper/2402.08846/) (2024)
- [A Survey on Speech Large Language Models for Understanding](https://mlsanity.com/paper/2410.18908/) (2024)
- [SLM: Bridge the thin gap between speech and text foundation models](https://mlsanity.com/paper/2310.00230/) (2023)
- [WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2203.15455/) (2022)
- [Joint Contextual Modeling for ASR Correction and Language Understanding](https://mlsanity.com/paper/2002.00750/) (2020)
- [ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets](https://mlsanity.com/paper/2406.08641/) (2024)
- [Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward](https://mlsanity.com/paper/2411.03866/) (2024)
- [Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation](https://mlsanity.com/paper/2102.07935/) (2021)

## Built on by (most cited first, 4 of 4)

- [On the Role of Encoder Depth: Pruning Whisper and LoRA Fine-Tuning in SLAM-ASR](https://mlsanity.com/paper/2603.27981/) (2026)
- [MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond](https://mlsanity.com/paper/2607.22100/) (2026)
- [Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning](https://mlsanity.com/paper/2603.06505/) (2026)
- [The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge](https://mlsanity.com/paper/2609.11724/) (2026)

## Abstract (arXiv)

In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM), which focuses on advancing multilingual conversational speech recognition through the development of speech language models architectures. Given the increasing relevance of real-world conversational data for building robust Spoken Dialogue Systems, we explore three approaches to multilingual ASR. First, we conduct an evaluation of the official baseline to better understand its strengths and limitations, by training two projectors (linear and qformer) with different foundation models. Second we leverage the SLAM-ASR framework to train a custom multilingual linear projector. Finally we investigate the role of contrastive learning and the extended conversational context in enhancing the robustness of recognition.