Recognizing emotions from human speech using 2-D neural classifier and influence the selection of input parameters on its accuracy

Author

Voznak, M. ; Partila, P. ; Mehic, Miralem ; Jakovlev, S.

Author_Institution

Fac. of Electr. Eng. & Comput. Sci., VSB-Tech. Univ. of Ostrava, Ostrava-Poruba, Czech Republic

fYear

2013

fDate

26-28 Nov. 2013

Firstpage

482

Lastpage

485

Abstract

This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations.

Keywords

emotion recognition; feature extraction; self-organising feature maps; speech recognition; 2D neural classifier; Kohohen self-organizing feature map; artificial neural nets; emotion classifier; emotion recognition; human speech; input parameter selection; neural network classifier; output-stage classifier; speech features extraction; Accuracy; Neural networks; Neurons; Speech; Speech processing; Speech recognition; Digital speech processing; emotions; fundamental frequency; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications Forum (TELFOR), 2013 21st

Conference_Location

Belgrade

Print_ISBN

978-1-4799-1419-7

Type

conf

DOI

10.1109/TELFOR.2013.6716272

Filename

6716272

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=675469