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- W1607388519 abstract "Human social communication depends largely on exchanges of non-verbal signals, including non-lexical expression of emotions in speech. In this work, we propose a biologically plausible methodology for the problem of emotion recognition, based on the extraction of vowel information from an input speech signal and on the classification of extracted information by a spiking neural network. Initially, a speech signal is segmented into vowel parts which are represented with a set of salient features, related to the Mel-frequency cesptrum. Different emotion classes are then recognized by a spiking neural network and classified into five different emotion classes." @default.
- W1607388519 created "2016-06-24" @default.
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- W1607388519 date "2006-01-01" @default.
- W1607388519 modified "2023-09-24" @default.
- W1607388519 title "Speech Emotion Recognition Using Spiking Neural Networks" @default.
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- W1607388519 doi "https://doi.org/10.1007/11875604_6" @default.
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