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Home > All Category Datasets > Speech Recognition Datasets > INTERSPEECH 2025 MLC-SLM Challenge Dataset

INTERSPEECH 2025 MLC-SLM Challenge Dataset

workshop audio dataset
mlc-slm dataset
ASR speech recognition data

The INTERSPEECH 2025 MLC-SLM Challenge Dataset, curated by Nexdata, is derived from fifteen proprietary conversational speech corpora. Distinguished by exceptional annotation accuracy and operational reliability, this dataset is engineered to address critical challenges in multilingual automatic speech recognition (ASR) and long-context comprehension. It meticulously replicates real-world complexities including spontaneous interruptions and speaker overlaps across 11 languages (1500 hours total duration), thereby providing robust training resources for developing world-ready ASR systems. All data collection and processing strictly comply with international privacy regulations including GDPR, CCPA and PIPL, with rigorous protocols ensuring participant anonymity and ethical data usage throughout the lifecycle.

Paid Datasets
This is a paid dataset licensed for commercial use. Ready-made datasets are available for immediate integration into AI projects.
SpecificationsSpecifications
Format
16kHz, 16bit, uncompressed wav, mono channel;
Recording Environment
quiet indoor environment, without echo;
Recording content
dozens of topics are specified, and the speakers make dialogue under those topics while the recording is performed;
Annotation
annotating for the transcription text, speaker identification, gender;
Device
Android mobile phone, iPhone;
Language
American English/British English/Filipino English/Australian English/Indian English/French/German/Italian/Japanese/Korean/Portuguese(Europe)/Russian/Spanish(Spain)/Thai/Vietnamese.
Sample Sample
  • Audio

    one direction is the first thing like in the mind

  • Audio

    Parce que j'ai plus l'ancien, j'en ai que celui-là dorénavant.

  • Audio

    D'accord très bien l'autre, je vais l'effacer alors.

  • Audio

    조금 이제 날씨도 더워지는데 덜 답답하구

  • Audio

    이천치십 년이랑 이천이십일 년 진짜 학교 못 간게

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The INTERSPEECH 2026 MLC-SLM Challenge Dataset, curated by Datatang, is derived from fifteen proprietary conversational speech corpora. Distinguished by exceptional annotation accuracy and operational reliability, this dataset is engineered to address critical challenges in multilingual automatic speech recognition (ASR) and long-context comprehension. It meticulously replicates real-world complexities including spontaneous interruptions and speaker overlaps, thereby providing robust training resources for developing world-ready ASR systems. All data collection and processing strictly comply with international privacy regulations including GDPR, CCPA and PIPL, with rigorous protocols ensuring participant anonymity and ethical data usage throughout the lifecycle.

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Dataset FAQs

What languages and scenarios are covered by Nexdata’s speech recognition datasets?

Nexdata offers speech recognition datasets covering a broad range of languages, dialects, and accents, backed by extensive global language resources. Our datasets include diverse speakers, acoustic environments, and real-world speech scenarios, supporting multilingual ASR, voice assistants, conversational AI, speech-to-text, and other speech-enabled applications.

Can Nexdata customize speech recognition datasets for specific languages or requirements?

Yes. If our off-the-shelf datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, transcription, and annotation services. We can customize datasets based on target languages or dialects, speaker profiles, recording environments, speech scenarios, data volume, and annotation specifications to meet specific ASR development needs.

How does Nexdata ensure the quality and scalability of speech recognition datasets?

Nexdata applies multi-stage quality control throughout speech data collection, transcription, annotation, and validation. Combined with our extensive language resources and scalable collection capabilities, we can support both large-scale multilingual projects and specialized datasets for specific languages, dialects, accents, and speech scenarios.

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