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

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...

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Bibliographic Details
Published in2013 21st Telecommunications Forum Telfor (TELFOR) pp. 482 - 485
Main Authors Voznak, M., Partila, P., Mehic, M., Jakovlev, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Accuracy
Digital speech processing
emotions
fundamental frequency
neural network
Neural networks
Neurons
Speech
Speech processing
Speech recognition
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Summary: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.
DOI:10.1109/TELFOR.2013.6716272