J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
<|reference_start|>J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling: Spoken dialogue plays a crucial role in human-AI interactions, necessitating dialogue-oriented spoken language models (SLMs). To develop versatile SLMs, large-scale and diverse speech datasets are essential. Additionally, to ensure hiqh-quality speech generation, the data must be spontaneous like in-wild data and must be acoustically clean with noise removed. Despite the critical need, no open-source corpus meeting all these criteria has been available. This study addresses this gap by constructing and releasing a large-scale spoken dialogue corpus, named Japanese Corpus for Human-AI Talks (J-CHAT), which is publicly accessible. Furthermore, this paper presents a language-independent method for corpus construction and describes experiments on dialogue generation using SLMs trained on J-CHAT. Experimental results indicate that the collected data from multiple domains by our method improve the naturalness and meaningfulness of dialogue generation.<|reference_end|>
@article{nakata2024j-chat:,
    title={J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue
  Language Modeling},
    author={Wataru Nakata, Kentaro Seki, Hitomi Yanaka, Yuki Saito, Shinnosuke
  Takamichi, Hiroshi Saruwatari},
    journal={arXiv preprint arXiv:2407.15828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15828},
    primaryClass={cs.CL cs.SD eess.AS}
}