@inproceedings{lebourdais-etal-2024-automatic-speech,
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Speech Interruption Detection: Analysis, Corpus, and System
%A Lebourdais, Martin
%A Tahon, Marie
%A Laurent, Antoine
%A Meignier, Sylvain
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F lebourdais-etal-2024-automatic-speech
%X 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.
%U https://aclanthology.org/2024.lrec-main.176
%P 1959-1968
Markdown (Informal)
[Automatic Speech Interruption Detection: Analysis, Corpus, and System](https://aclanthology.org/2024.lrec-main.176) (Lebourdais et al., LREC-COLING 2024)
ACL