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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Buscicchio, C. A., Górecki, P., & Caponetti, L. (2006). Speech emotion recognition using spiking neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4203 LNAI, pp. 38–46). Springer Verlag. https://doi.org/10.1007/11875604_6
Mendeley helps you to discover research relevant for your work.