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Robust machine learning methods for computational paralinguistics and multimodal affective computing

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dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Salah, Albert Ali.
dc.contributor.advisor Gürgen, Fikret.
dc.contributor.author Kaya, Heysem.
dc.date.accessioned 2023-03-16T10:13:43Z
dc.date.available 2023-03-16T10:13:43Z
dc.date.issued 2015.
dc.identifier.other CMPE 2015 K38 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12600
dc.description.abstract The analysis of a ect (e.g. emotions or mood), traits (e.g. personality), and social signals (e.g. frustration, disagreement) are of increasing interest in human computer interaction, in order to drive human-machine communication to become closer to human-human communication. It has manifold applications ranging from intelligent tutoring systems to a ect sensitive robots, from smart call centers to patient telemonitoring. The study of computational paralinguistics, which covers the analysis of speaker states and traits, faces with real life challenges of inter-speaker and inter-corpus variability. In this thesis, machine learning methods addressing these challenges are targeted. Automatic model selection methods are explored for modeling high dimensional paralinguistics data. These approaches can deal with di erent sources of variability in a parametric manner. To provide statistical models and classi ers with a compact set of potent features, novel feature selection methods based on discriminative projections are introduced. In addition, multimodal fusion techniques are sought for robust a ective computing in the wild. The proposed methods and approaches are validated over a set of recent challenge corpora, including INTERSPEECH Computational Paralinguistics Challenge (2013-2015), Audio-Visual Emotion Challenge (2013/2014), and Emotion Recognition in the Wild Challenge 2014. The methods proposed in this thesis advance the state-of-the-art in most of these corpora and yield competitive results in others, while enjoying the properties of parsimony and computational e ciency.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2015.
dc.subject.lcsh Human-robot interaction
dc.title Robust machine learning methods for computational paralinguistics and multimodal affective computing
dc.format.pages xviii, 157 leaves ;


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