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dc.contributor.authorBaumann, Timo
dc.contributor.authorSiegert, Ingo
dc.contributor.editorAlt, Florian
dc.contributor.editorSchneegass, Stefan
dc.contributor.editorHornecker, Eva
dc.date.accessioned2020-09-16T07:52:29Z
dc.date.available2020-09-16T07:52:29Z
dc.date.issued2020
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/34263
dc.description.abstractWe analyze the addressee detection task for complexityidentical dialog for both human conversation and devicedirected speech. Our recurrent neural model performs at least as good as humans, who have problems with this task, even native speakers, who profit from the relevant linguistic skills. We perform ablation experiments on the features used by our model and show that fundamental frequency variation is the single most relevant feature class. Therefore, we conclude that future systems can detect whether they are addressed based only on speech prosody which does not (or only to a very limited extent) reveal the content of conversations not intended for the system.en
dc.description.urihttps://dl.acm.org/doi/10.1145/3404983.3410021en
dc.language.isoen
dc.publisherACM
dc.relation.ispartofMensch und Computer 2020 - Tagungsband
dc.relation.ispartofseriesMensch und Computer
dc.subjectfundamental frequency variation
dc.subjectaddressee detection
dc.subjectrecurrent neural network
dc.subjectcomplexity-identical human-computer interaction
dc.subjectcomputational paralinguistics
dc.titleProsodic addressee-detection: ensuring privacy in always-on spoken dialog systemsen
dc.typeText/Conference Paper
dc.pubPlaceNew York
mci.document.qualitydigidoc
mci.reference.pages195–198
mci.conference.sessiontitleMCI: Short Paper (Poster)
mci.conference.locationMagdeburg
mci.conference.date6.-9. September 2020
dc.identifier.doi10.1145/3404983.3410021


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