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- W1490990698 abstract "In this work, a novel method for system fusion in emotion recognition for speech is presented. The proposed approach, namely Anchor Model Fusion (AMF), exploits the characteristic behaviour of the scores of a speech utterance among different emotion models, by a mapping to a back-end anchor-model feature space followed by a SVM classifier. Experiments are presented in three different databases: Ahumada III, with speech obtained from real forensic cases; and SUSAS Actual and SUSAS Simulated. Results comparing AMF with a simple sum-fusion scheme after normalization show a significant performance improvement of the proposed technique for two of the three experimental set-ups, without degrading performance in the third one." @default.
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- W1490990698 date "2009-01-01" @default.
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- W1490990698 title "Anchor Model Fusion for Emotion Recognition in Speech" @default.
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- W1490990698 doi "https://doi.org/10.1007/978-3-642-04391-8_7" @default.
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