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Musical-Linguistic Annotations of Il Lauro Secco ...
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Musical-Linguistic Annotations of Il Lauro Secco ...
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Selecting training data for cross-corpus speech emotion recognition: Prototypicality vs. generalization
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
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
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
In: http://www.mmk.ei.tum.de/publ/pdf/11/11woe6.pdf (2011)
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7
AcousticLinguistic Recognition of Interest in Speech with BottleneckBLSTM Nets
In: http://www.mmk.ei.tum.de/publ/pdf/11/11woe7.pdf (2011)
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8
Word Accent and Emotion
In: http://www.mmk.ei.tum.de/publ/pdf/10/10sep1.pdf (2010)
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
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
In: http://www.mmk.ei.tum.de/publ/pdf/10/10sch5.pdf (2010)
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10
The interspeech 2009 emotion challenge
In: http://www.mmk.ei.tum.de/publ/pdf/09/09sch19.pdf (2009)
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11
A.: The interspeech 2009 emotion challenge
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
In: http://www.mmk.ei.tum.de/publ/pdf/10/10ste1.pdf (2009)
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13
Comparing one and two-stage acoustic modeling in the recognition of emotion
In: http://www.mmk.ei.tum.de/publ/pdf/07/07sch9.pdf (2007)
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14
Speaker Independent Emotion Recognition by Early Fusion of Acoustic and Linguistic Features within Ensembles
In: http://www.mmk.ei.tum.de/publ/pdf/05/05sch4.pdf (2005)
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15
Metaclassifiers in acoustic and linguistic feature fusion-based affect recognition
In: http://www.mmk.ei.tum.de/publ/pdf/05/05sch1.pdf (2005)
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16
Speech emotion recognition exploiting acoustic and linguistic information sources
In: http://www.mmk.ei.tum.de/publ/pdf/05/05rig2.pdf (2005)
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17
Hidden Markov ModelBased Speech Emotion Recognition
In: http://www.mmk.ei.tum.de/publ/pdf/03/03sch1.pdf (2003)
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18
M.: “Hidden Markov Model-Based Speech Emotion Recognition
In: http://www.mmk.ei.tum.de/publ/pdf/03/03sch2.pdf (2003)
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19
Balancing Spoken Content Adaptation and Unit Length in the Recognition of Emotion and Interest
In: http://www.mmk.ei.tum.de/publ/pdf/08/08vla2.pdf
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20
Communication
In: http://www.mmk.e-technik.tu-muenchen.de/publ/pdf/07/07sch8.pdf
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