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- W1532794994 abstract "It is widely accepted that the ability to understand emotion or affect from speech is central to the design of more natural human-computer interfaces. This paper explores the classification of natural emotional speech along four affective dimensions, using hidden Markov models (HMMs). A number of features are tested, some of which have never before been applied to emotion recognition. Finally, these different features are combined discriminatively to achieve a competitive performance on the AVEC 2011 affect classification task [1]." @default.
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- W1532794994 date "2013-09-01" @default.
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- W1532794994 title "Late integration of features for acoustic emotion recognition" @default.
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