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Selecting training data for cross-corpus speech emotion recognition: Prototypicality vs. generalization
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11sch4.pdf (2011)
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Speech-based non-prototypical affect recognition for child-robot interaction in reverberated environments
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11woe8.pdf (2011)
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Localization of non-linguistic events in spontaneous speech by non-negative matrix factorization and long short-term memory
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11wen1.pdf (2011)
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Robust multi-stream keyword and non-linguistic vocalization detection for computationally intelligent virtual agents
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11woe6.pdf (2011)
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AcousticLinguistic Recognition of Interest in Speech with BottleneckBLSTM Nets
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11woe7.pdf (2011)
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Word Accent and Emotion
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In: http://www.mmk.ei.tum.de/publ/pdf/10/10sep1.pdf (2010)
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Abstract:
In this paper, we address the question whether prosodically/linguistically prominent syllables carrying the word accent (stressed syllables) are better indicators for emotional marking than unstressed syllables. To this aim, we use a large spontaneous database with children interacting with Sony’s Aibo robot, annotated with word-based emotion labels, large acoustic-prosodic feature vectors, and support vector machines as classifiers. It turns out that, in most of the cases, stressed syllables are better emotion markers than unstressed syllables. Moreover, we discuss specific phenomena such as vocatives and other constellations, to be modelled in future studies. Index Terms: emotion, linguistics, paralinguistics, word accent, lexical stress, automatic classification
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URL: http://www.mmk.ei.tum.de/publ/pdf/10/10sep1.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.4841
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Discrimination of speech and non-linguistic vocalizations by non-negative matrix factorization
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In: http://www.mmk.ei.tum.de/publ/pdf/10/10sch5.pdf (2010)
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The interspeech 2009 emotion challenge
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In: http://www.mmk.ei.tum.de/publ/pdf/09/09sch19.pdf (2009)
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A.: The interspeech 2009 emotion challenge
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In: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2009/Schuller09-TI2.pdf (2009)
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doi:10.1155/2010/783954 Research Article On the Impact of Children’s Emotional Speech on Acoustic and Language Models
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In: http://www.mmk.ei.tum.de/publ/pdf/10/10ste1.pdf (2009)
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Comparing one and two-stage acoustic modeling in the recognition of emotion
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In: http://www.mmk.ei.tum.de/publ/pdf/07/07sch9.pdf (2007)
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Speaker Independent Emotion Recognition by Early Fusion of Acoustic and Linguistic Features within Ensembles
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In: http://www.mmk.ei.tum.de/publ/pdf/05/05sch4.pdf (2005)
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Metaclassifiers in acoustic and linguistic feature fusion-based affect recognition
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In: http://www.mmk.ei.tum.de/publ/pdf/05/05sch1.pdf (2005)
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Speech emotion recognition exploiting acoustic and linguistic information sources
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In: http://www.mmk.ei.tum.de/publ/pdf/05/05rig2.pdf (2005)
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Hidden Markov ModelBased Speech Emotion Recognition
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In: http://www.mmk.ei.tum.de/publ/pdf/03/03sch1.pdf (2003)
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M.: “Hidden Markov Model-Based Speech Emotion Recognition
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In: http://www.mmk.ei.tum.de/publ/pdf/03/03sch2.pdf (2003)
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Balancing Spoken Content Adaptation and Unit Length in the Recognition of Emotion and Interest
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In: http://www.mmk.ei.tum.de/publ/pdf/08/08vla2.pdf
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Communication
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In: http://www.mmk.e-technik.tu-muenchen.de/publ/pdf/07/07sch8.pdf
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