Interspeech2025-MLC-SLM-Challenge

The Interspeech 2025 Multilingual Conversational Speech LLM (MLC-SLM) Challenge

Motivation

Large Language Models (LLMs) have demonstrated remarkable capabilities in a wide range of downstream tasks, serving as powerful foundation models for language understanding and generation. Furthermore, there has been significant attention on utilizing LLMs in speech and audio processing tasks such as Automatic Speech Recognition (ASR), Audio Captioning, and emerging areas like Spoken Dialogue Models.

However, real-world conversational speech data is critical for the development of robust LLM-based Spoken Dialogue Models, as it encapsulates the complexity of human communication, including natural pauses, interruptions, speaker overlaps, and diverse conversational styles. The limited availability of such data, especially in multilingual settings, poses a significant challenge to advancing the field.

The importance of real-world conversational speech extends beyond technological advancement—it is essential for building AI systems that can understand and respond naturally in multilingual, dynamic, and context-rich environments. This is especially crucial for next-generation human-AI interaction systems, where spoken dialogue serves as a primary mode of communication.

Thus, this workshop aims to bridge the gap by hosting the challenge of building multilingual conversational speech language models together with the release of a real-world multilingual conversational speech dataset.

Task Setting

The event consists of two tasks, both of which require participants to explore the development of speech language model:

Task 1: Multilingual Conversational Speech Recognition

Participants will be provided with oracle segmentation for each conversation.

Objective: Develop a multilingual LLM based ASR model

This task focuses on optimizing transcription accuracy in a multilingual setting.

Task 2: Multilingual Conversational Speech Diarization and Recognition

No prior or oracle information will be provided during evaluation (e.g., no pre-segmented utterances or speaker labels).

Objective: Develop a system for both speaker diarization (identifying who is speaking when), and recognition (transcribing speech to text).

Both pipeline-based and end-to-end systems are encouraged, providing flexibility in system design and implementation.

Important Dates

February 20, 2025: Registration opens

March 10, 2025: Training data release

March 17, 2025: Development set and baseline system release

May 15, 2025: Evaluation set release and leaderboard open

June 1, 2025: Leaderboard freeze and submission portal opens (CMT system)

June 20, 2025: Submission deadline

July 10, 2025: Notification of acceptance

August 22, 2025: Workshop date

Dataset Description

The challenge dataset comprises approximately 11 languages: English (en), French (fr), German (de), Italian (it), Portuguese (pt), Spanish (es), Japanese (jp), Korean (ko), Russian (ru), Thai (th), Vietnamese (vi)

Each set consists of two-speaker conversational speech on randomly assigned topics.

Conversations are natural and fluent, with speakers engaging in meaningful dialogues on each topic.

Recorded in quiet indoor environments using devices such as iPhone.

The English dataset comprises approximately 500 hours of recordings from various regions, including British, American, Australian, Indian, and Philippine English. Other languages contribute around 100 hours each, resulting in a total of approximately 1500 hours of multilingual conversational speech data.

Registration

To participate, registration is required. Please upload signed Data use agreement and complete the registration form before April 1, 2025. Note that this does not means the challenge starts on April 1, 2025. The challenge begins on February 20, 2025.

For any other information about registration, please send Email to: [email protected]

More details：https://www.nexdata.ai/competition/mlc-slm