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• W2955755603 abstract "Contemporary technical devicesBöck, Ronald obey the paradigm of naturalistic multimodal interaction and user-centric individualisation. Users expect devices to interact intelligently, toEgorow, Olga anticipate their needs, and to adapt to their behaviour. To do so, companion-like solutions have to take into account the affectiveHöbel-Müller, Juliane and dispositional state of the user, and therefore to be trained and modified using interaction data and corpora. We argue that, inRequardt, Alicia Flores this context, big data alone is not purposeful, since important effects are obscured, and since high-quality annotation is too costly. We encourage the collection and use of enriched data. We report on recent trends in this field, presenting methodologies for collecting data with rich disposition variety and predictable classifications based on a careful design and standardisedSiegert, Ingo psychological assessments. Besides socio-demographic information and personality traits, we alsoWendemuth, Andreas use speech events to improve user state models. Furthermore, we present possibilities to increase the amount of enriched data in cross-corpus or intra-corpus way based on recent learning approaches. Finally, we highlight particular recent neural recognition approaches feasible for smaller datasets, and covering temporal aspects." @default.
• W2955755603 created "2019-07-12" @default.
• W2955755603 creator A5033995312 @default.
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• W2955755603 date "2019-01-01" @default.
• W2955755603 modified "2023-09-26" @default.
• W2955755603 title "Anticipating the User: Acoustic Disposition Recognition in Intelligent Interactions" @default.
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