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.
Keywords
- Speech Signal
- Emotion Recognition
- Spike Train
- Interactive Voice Response System
- Spike Neural Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Buscicchio, C.A., Górecki, P., Caponetti, L. (2006). Speech Emotion Recognition Using Spiking Neural Networks. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_6
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DOI: https://doi.org/10.1007/11875604_6
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