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Combining Mimic and Prosodic Analyses for User Disposition Classification

, , , , , and . Elektronische Sprachsignalverarbeitung 2012. Tagungsband der 23. Konferenz, volume 64 of Studientexte zur Sprachkommunikation, page 220--227. Cottbus, Germany, TUDpress, (2012)

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Incorporation of a mood-model to improve user-disposition prediction from emotion recognition, , and . Program and Abstracts of the COST 2102 Final Conference held in conjunction with the 4th COST 2102 International Training School on COGNITIVE BEHAVIOURAL SYSTEMS, Dresden, Gemany, Dresden : Inst. für Akustik und Sprachkommunikation, (February 2011)Investigation of Hierarchical Classification for Simultaneous Gender and Age Recognitions, , , and . Elektronische Sprachsignalverarbeitung 2012. Tagungsband der 23. Konferenz, volume 64 of Studientexte zur Sprachkommunikation, page 58--65. Leipzig, Germany, TUDpress, (2016)Modeling phonetic pattern variability in favor of the creation of robust emotion classifiers for real-life applications., , , and . Comput. Speech Lang., 28 (2): 483-500 (2014)ERM4HCI 2013: the 1st workshop on emotion representation and modelling in human-computer-interaction-systems., , , , , and . ICMI, page 607-608. ACM, (2013)Modelling Contexts for Interactions in Dynamic Open-World Scenarios., and . SMC, page 1459-1464. IEEE, (2019)Combining Mimic and Prosodic Analyses for User Disposition Classification, , , , , and . Elektronische Sprachsignalverarbeitung 2012. Tagungsband der 23. Konferenz, volume 64 of Studientexte zur Sprachkommunikation, page 220--227. Cottbus, Germany, TUDpress, (2012)ikannotate - A Tool for Labelling, Transcription, and Annotation of Emotionally Coloured Speech, , , , and . Affective Computing and Intelligent Interaction., volume 6974 of Lecture Notes in Computer Science, Springer Berlin, Heidelberg, (2011)Towards True Artificial Peers., , and . ICHMS, page 1-5. IEEE, (2020)On the Sound of Successful Meetings: How Speech Prosody Predicts Meeting Performance., , and . ICMI Companion, page 240-248. ACM, (2021)Recognition of emotional speech with convolutional neural networks by means of spectral estimates., , and . ACII Workshops, page 50-55. IEEE Computer Society, (2017)