@inproceedings{chen-etal-2025-interactspeech,
    title = "{I}nteract{S}peech: A Speech Dialogue Interaction Corpus for Spoken Dialogue Model",
    author = "Chen, Yifu  and
      Ji, Shengpeng  and
      Wang, Ziqing  and
      Wang, Hanting  and
      Zhao, Zhou",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.424/",
    doi = "10.18653/v1/2025.findings-emnlp.424",
    pages = "8024--8033",
    ISBN = "979-8-89176-335-7",
    abstract = "Spoken Dialogue Models (SDMs) have achieved significant progress in recent years, yet they continue to face challenges in handling nuanced interactional phenomena. A significant bottleneck hindering further advancement is the scarcity of publicly available, high-quality datasets meticulously designed to train and evaluate these fine-grained interactive capabilities. We introduce InteractSpeech, a 150-hour English speech interaction dialogue dataset designed to empower spoken dialogue models with nuanced real-time interaction capabilities, such as handling interruptions and backchannels. InteractSpeech was created by synthesizing interactive dialogues from text using advanced speech synthesis, and by filtering real-world spoken dialogues for interactive segments. The dataset features precise speaker timestamps and annotations for diverse dialogue interactions, underpinned by a formal framework for interaction dynamics. We demonstrate InteractSpeech{'}s utility by fine-tuning a LLaMA 3-8B model on its textual scenarios and, crucially, by training a speech understanding model that accurately classifies key interactional events directly from audio. This highlights the dataset{'}s value in developing models capable of more natural and responsive conversational turn-taking. Audio samples are available at https://interactspeech.github.io/."
}