mlsanityData to 7 Oct 2026

J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling

arXiv 2407.15828 ↗First posted 22 Jul 2024cs.CL cs.SD eess.AScited by 2

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Abstract (arXiv)

Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, high-quality, and diverse spoken dialogue corpora. However, existing datasets are often limited in size, spontaneity, or linguistic coherence. To address these limitations, we introduce J-CHAT, a 76,000-hour open-source Japanese spoken dialogue corpus. Constructed using an automated, language-independent methodology, J-CHAT ensures acoustic cleanliness, diversity, and natural spontaneity. The corpus is built from YouTube and podcast data, with extensive filtering and denoising to enhance quality. Experimental results with generative spoken dialogue language models trained on J-CHAT demonstrate its effectiveness for SDS development. By providing a robust foundation for training advanced dialogue models, we anticipate that J-CHAT will drive progress in human-AI dialogue research and applications.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

22 of 34 references.

References · 22

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10 more
  1. 2023Powerset multi-class cross entropy loss for neural speaker diarizationcited by 109
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Towards a Japanese Full-duplex Spoken Dialogue Systemcited by 5
  2. 2026DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modelingcited by 3