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dc.contributor.authorHuber, Bernd
dc.contributor.authorDavis, Richard F.
dc.contributor.authorCotter, Allison
dc.contributor.authorJunkin, Emily
dc.contributor.authorYard, Mindy
dc.contributor.authorShieber, Stuart
dc.contributor.authorBrestan-Knight, Elizabeth
dc.contributor.authorGajos, Krzysztof
dc.date.accessioned2019-07-03T15:07:05Z
dc.date.issued2019-05
dc.identifier.citationHuber, Bernd, Richard David, Allison Cotter, Emily Junkin, Mindy Yard, Stuart M. Shieber, Elizabeth Brestan-Knight, and Krzysztof Gajos. 2019. SpecialTime: Automatically Detecting Dialogue Acts from Speech to Support Parent-Child Interaction Therapy. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth'19), Trento, Italy, May 20-23, 2019.en_US
dc.identifier.isbn9781450361262en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:40827360*
dc.description.abstractParent-child interaction therapy (PCIT) helps parents improve the quality of interaction with children who have behavior problems. The therapy trains parents to use effective dialogue acts when interacting with their children. Besides weekly coaching by therapists, the therapy relies on deliberate practice of skills by parents in their homes. We developed SpecialTime, a system that provides parents engaged in PCIT with automatic, real-time feedback on their dialogue act use. To do this, we first created a dataset of 6,022 parent dialogue acts, annotated by experts with dialogue act labels that therapists use to code parent speech. We then developed an algorithm that classifies the dialogue acts into 8 classes with an overall accuracy of 78%. To test the system in an actual clinical setting, we conducted a one month pilot study with four parents currently in therapy. The results suggest that automatic feedback on spoken dialogue acts is possible in PCIT, and that parents find the automatic feedback useful.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dc.publisherACM Pressen_US
dc.relationProceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth '19)en_US
dc.relation.hasversionhttp://scholar.harvard.edu/files/bhb/files/specialtime_final_pervasivehealth19.pdfen_US
dash.licenseOAP
dc.titleSpecialTime: Automatically Detecting Dialogue Acts from Speech to Support Parent-Child Interaction Therapyen_US
dc.title.alternativeAutomatically Detecting Dialogue Acts from Speech to Support Parent-Child Interaction Therapy
dc.typeConference Paperen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalProceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcareen_US
dc.date.available2019-07-03T15:07:05Z
dash.affiliation.otherHarvard John A. Paulson School of Engineering and Applied Sciencesen_US
dc.data.urihttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/C5Z3SC
dc.identifier.doi10.1145/3329189.3329203
dash.contributor.affiliatedHuber, Bernd
dash.contributor.affiliatedShieber, Stuart
dash.contributor.affiliatedGajos, Krzysztof


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