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dc.contributor.author
Brusco, Pablo
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dc.contributor.author
Gravano, Agustin
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dc.date.available
2023-12-07T11:53:45Z
dc.date.issued
2023-03
dc.identifier.citation
Brusco, Pablo; Gravano, Agustin; Automatic offline annotation of turn-taking transitions in task-oriented dialogue; Academic Press Ltd - Elsevier Science Ltd; Computer Speech And Language; 78; 3-2023; 1-19
dc.identifier.issn
0885-2308
dc.identifier.uri
http://hdl.handle.net/11336/219622
dc.description.abstract
As the volume of recorded conversations continues to surge, so does the need for their automatic processing. Plenty of information beyond words may be extracted from the speech signal that could be valuable in domains such as call-center quality assurance. In particular, describing the dynamics of turn-taking exchanges allows for a deeper understanding of the development and outcome of a dialogue. In this paper, we investigate the construction of an automatic turn-taking annotation tool based on recordings of entire conversations (in offline mode) — an unexplored topic to our knowledge. We experiment with two supervised learning approaches, using recurrent neural networks and random forests, on a corpus of Argentine Spanish task-oriented dialogues annotated with 12 turn-taking categories following standard guidelines. Our models achieve promising results, with F1 scores ranging 0.7–0.9 for the most frequent labels (e.g., smooth switches, backchannels), but much lower for the least frequent ones (various kinds of interruptions), for which further research is needed. We also evaluate our best-performing models considering their generalizability in scenarios of growing difficulty, including dialogues in two different languages (English and Slovak). Finally, to address the typical data scarcity issue, we analyze the impact of combining training data from different corpora, again including cross-linguistic data.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Academic Press Ltd - Elsevier Science Ltd
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DIALOGUE
dc.subject
MACHINE LEARNING
dc.subject
OFFLINE AUDIO PROCESSING
dc.subject
TURN-TAKING
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Automatic offline annotation of turn-taking transitions in task-oriented dialogue
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-12-06T15:03:46Z
dc.journal.volume
78
dc.journal.pagination
1-19
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Brusco, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
dc.description.fil
Fil: Gravano, Agustin. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Computer Speech And Language
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0885230822000857
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.csl.2022.101462
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