Automatic Speech Interruption Detection: Analysis, Corpus, and SystemMartin Lebourdais, Marie Tahon, Antoine Laurent, Sylvain Meignier

IRIT-SAMoVA - Équipe Structuration, Analyse et MOdélisation de documents Vidéo et Audio

  LIUM - Laboratoire d'Informatique de l'Université du Mans , LST - Equipe Language and Speech Technology



Contact: martin.lebourdais[@]rit.fr ; {marie.tahon, antoine.laurent, sylvain.meignier}@univ-lemans.fr




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. 




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