J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
arXiv:2407.15828v2 Announce Type: replace-cross Abstract: 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 m
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Abstract: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.
Comments: 8 pages, 3 figures
Subjects:
Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2407.15828 [cs.CL]
(or arXiv:2407.15828v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2407.15828
arXiv-issued DOI via DataCite
Submission history
From: Wataru Nakata [view email] [v1] Mon, 22 Jul 2024 17:46:50 UTC (4,158 KB) [v2] Thu, 2 Apr 2026 09:29:59 UTC (396 KB)
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Cheaper/faster/easier makes for step changes (and that's why even current-level LLMs are transformative)
We already knew there's nothing new under the sun. Thanks to advances in telescopes, orbital launch, satellites, and space vehicles we now know there's nothing new above the sun either, but there is rather a lot of energy! For many phenomena, I think it's a matter of convenience and utility where you model them as discrete or continuous, aka, qualitative vs quantitative. On one level, nukes are simply a bigger explosion, and we already had explosions. On another level, they're sufficiently bigger as to have reshaped global politics and rewritten the decision theory of modern war. Perhaps the key thing is remembering that sufficiently large quantitative changes can make for qualitative macro effects. For example, basic elements of modern life include transport, communication, energy, comput





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