  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.
