J-CHAT：用于 spoken dialogue 语言建模的日本大规模口语对话语料库
J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
AI总结：
本文提出 J-CHAT，一个76000小时的日本口语对话语料库，用于改进 spoken dialogue 语言模型，通过自动化方法确保高质量和自然性，推动人机对话研究。
摘要：
口语对话对于人机交互至关重要，提供了超越文本的表达能力。开发有效的口语对话系统（SDSs）需要大规模、高质量和多样化的口语对话语料库。然而，现有数据集往往在规模、自发性和语言连贯性上有限。为了解决这些限制，我们引入了 J-CHAT，一个 76,000 小时的开源日本口语对话语料库。该语料库通过自动化、语言无关的方法构建，确保了语音清洁度、多样性和自然自发性。语料库来源于 YouTube 和播客数据，经过广泛的过滤和降噪处理以提高质量。在使用生成式口语对话语言模型训练于 J-CHAT 的实验结果表明，其在 SDS 开发中是有效的。通过为训练高级对话模型提供坚实的基座，我们预计 J-CHAT 将推动人机对话研究和应用的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
