Show simple item record

dc.contributor.authorRoux, Frédéric
dc.contributor.authorArmstrong, Blair C.
dc.contributor.authorCarreiras, Manuel
dc.date.accessioned2017-11-16T12:55:55Z
dc.date.available2017-11-16T12:55:55Z
dc.date.issued2017
dc.identifier.citationRoux, F., Armstrong, B.C. & Carreiras, M. Behav Res (2017) 49: 1864. https://doi.org/10.3758/s13428-016-0830-1es_ES
dc.identifier.issn1554-351X
dc.identifier.urihttp://hdl.handle.net/10810/23505
dc.descriptionPublished online: 6 December 2016es_ES
dc.description.abstractThe analysis of speech onset times has a longstanding tradition in experimental psychology as a measure of how a stimulus influences a spoken response. Yet the lack of accurate automatic methods to measure such effects forces researchers to rely on time-intensive manual or semiautomatic techniques. Here we present Chronset, a fully automated tool that estimates speech onset on the basis of multiple acoustic features extracted via multitaper spectral analysis. Using statistical optimization techniques, we show that the present approach generalizes across different languages and speaker populations, and that it extracts speech onset latencies that agree closely with those from human observations. Finally, we show how the present approach can be integrated with previous work (Jansen & Watter Behavior Research Methods, 40:744–751, 2008) to further improve the precision of onset detection. Chronset is publicly available online at www.bcbl.eu/databases/chronset.es_ES
dc.description.sponsorshipWe thank P. Jansen and two anonymous reviewers for their constructive comments and for helping us improve the present article, especially by motivating the mixture-of-experts model and our discussion of the merits of different measures of model fit. F.R. and B.C.A. were both supported by Marie Sktodowska-Curie grants (to F.R., PIEFGA- 2013-62772; to B.C.A., PIIF-GA-2013-627784). M.C. was supported by the BCBL and Ikerbasque, the Basque Foundation for Science, and the European Research Council (Grant No. ERC-2011-ADG-295362). B.C.A. and M.C. were also supported by the Severo Ochoa program, Grant No. SEV-2015-049 awarded to the BCBL.es_ES
dc.language.isoenges_ES
dc.publisherBehavior Research Methodses_ES
dc.relationinfo:eu-repo/grantAgreement/EC/PIEFGA- 2013-62772es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/PIIF-GA-2013-627784es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/ERC-2011-ADG-295362es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/SEV-2015-0490es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectSpeech onsetes_ES
dc.subjectReading aloudes_ES
dc.subjectAutomatic detectiones_ES
dc.subjectSpectral analysises_ES
dc.subjectOptimizationes_ES
dc.titleChronset: An automated tool for detecting speech onsetes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© The Author(s) 2016. This article is published with open access at Springerlink.com Open Access This article is distributed under the terms of the Creative Commons At tribution 4.0 International License (http:/ / creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.es_ES
dc.relation.publisherversionhttps://link.springer.com/journal/13428es_ES
dc.identifier.doi10.3758/s13428-016-0830-1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record