Multilingual challenge advances conversational speech recognition and understanding
The Second MLC-SLM Challenge: Multilingual Conversational Speech Diarization, Recognition, and Understanding
Sound
Summary
Figuring out who is speaking, what they say, and what it means is hard when people talk in multiple languages. This paper sums up a global challenge where teams worked on solving these problems using real conversations from everyday life. The challenge offered two main tasks: recognizing and separating speech by speakers in many languages, and understanding the meaning behind their speech. The authors provide details about the data, rules, and starting systems, and share what they learned from the best teams to help future work.
What this means in practice
- •For call center operators: Improve automated tracking of who spoke and what was said across multiple languages in real customer conversations.
- •For language technology developers: Build better multilingual speech systems by using the shared dataset and evaluation methods from the challenge to test and improve models.
Authors
Bingshen Mu, Mingchen Shao, Zhennan Lin, Liumeng Xue, Hexin Liu, Lei Xie, Eng Siong Chng, Longshuai Xiao, Qiangze Feng, Daliang Wang
Abstract
This paper summarizes the Interspeech2026 second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge, which aims to advance the development of effective multilingual conversational speech language models. We describe the two challenge tasks: multilingual conversational speech diarization and recognition, and multilingual conversational speech understanding, together with the released real-world conversational speech dataset, evaluation protocols, and baseline systems. The challenge attracted 91 teams worldwide, with 704 valid leaderboard results and 14 technical reports across the two tasks. Based on the participating systems, we summarize representative approaches and distill practical insights into multilingual conversational speech recognition and understanding to support future research in the community.