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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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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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Abstract:
The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus [1] serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech. Index Terms: emotion, challenge, feature types, classification 1.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.875 http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2009/Schuller09-TI2.pdf
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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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