# Summary on The Multilingual Conversational Speech Language Model Challenge: Datasets, Tasks, Baselines, and Methods

arXiv: https://arxiv.org/abs/2509.13785 · HTML: https://mlsanity.com/paper/2509.13785/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2025.

## Built on by (most cited first, 10 of 10)

- [Qwen3-ASR Technical Report](https://mlsanity.com/paper/2601.21337/) (2026)
- [VIBEVOICE-ASR Technical Report](https://mlsanity.com/paper/2601.18184/) (2026)
- [Qwen3.8-Omni: Towards Native Omni-Modal Agents](https://mlsanity.com/paper/2609.25611/) (2026)
- [Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning](https://mlsanity.com/paper/2603.06505/) (2026)
- [G-STAR: End-to-End Global Speaker-Tracking Attributed Recognition](https://mlsanity.com/paper/2603.10468/) (2026)
- [MUSCAT: MUltilingual, SCientific ConversATion Benchmark](https://mlsanity.com/paper/2604.15929/) (2026)
- [An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge](https://mlsanity.com/paper/2607.12468/) (2026)
- [The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge](https://mlsanity.com/paper/2609.11724/) (2026)
- [Bridging the Modality Gap in Long-Form Clinical Audio: A Comparative Study of Lightweight and Heavyweight End-to-End SOAP Generation](https://mlsanity.com/paper/2609.14467/) (2026)
- [The Bairong System for MLC-SLM 2026: Dynamic Question-Aware Evidence Routing for Multilingual Conversational Speech Understanding](https://mlsanity.com/paper/2609.22214/) (2026)

## Abstract (arXiv)

This paper summarizes the Interspeech2025 Multilingual Conversational Speech Language Model (MLC-SLM) challenge, which aims to advance the exploration of building effective multilingual conversational speech LLMs (SLLMs). We provide a detailed description of the task settings for the MLC-SLM challenge, the released real-world multilingual conversational speech dataset totaling approximately 1,604 hours, and the baseline systems for participants. The MLC-SLM challenge attracts 78 teams from 13 countries to participate, with 489 valid leaderboard results and 14 technical reports for the two tasks. We distill valuable insights on building multilingual conversational SLLMs based on submissions from participants, aiming to contribute to the advancement of the community.