@inproceedings{lebourdais-etal-2024-automatic,
    title = "Automatic Speech Interruption Detection: Analysis, Corpus, and System",
    author = "Lebourdais, Martin  and
      Tahon, Marie  and
      Laurent, Antoine  and
      Meignier, Sylvain",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.176",
    pages = "1959--1968",
    abstract = "Interruption detection is a new yet challenging task in the field of speech processing. This article presents a comprehensive study on automatic speech interruption detection, from the definition of this task, the assembly of a specialized corpus, and the development of an initial baseline system. We provide three main contributions: Firstly, we define the task, taking into account the nuanced nature of interruptions within spontaneous conversations. Secondly, we introduce a new corpus of conversational data, annotated for interruptions, to facilitate research in this domain. This corpus serves as a valuable resource for evaluating and advancing interruption detection techniques. Lastly, we present a first baseline system, which use speech processing methods to automatically identify interruptions in speech with promising results. In this article, we derivate from theoretical notions of interruption to build a simplification of this notion based on overlapped speech detection. Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection.",
}

Interruption detection is a new yet challenging task in the field of speech processing. This article presents a comprehensive study on automatic speech interruption detection, from the definition of this task, the assembly of a specialized corpus, and the development of an initial baseline system. We provide three main contributions: Firstly, we define the task, taking into account the nuanced nature of interruptions within spontaneous conversations. Secondly, we introduce a new corpus of conversational data, annotated for interruptions, to facilitate research in this domain. This corpus serves as a valuable resource for evaluating and advancing interruption detection techniques. Lastly, we present a first baseline system, which use speech processing methods to automatically identify interruptions in speech with promising results. In this article, we derivate from theoretical notions of interruption to build a simplification of this notion based on overlapped speech detection. Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection.
Lebourdais, Martin Tahon, Marie Laurent, Antoine Meignier, Sylvain
Automatic Speech Interruption Detection: Analysis, Corpus, and System