Emotion recognition from speech using Fisher´s discriminant analysis and Bayesian classifier

Author

Atasoy, Huseyin ; Yildirim, Serdar ; Yildirim, Esen

Author_Institution

Bilgisayar Muhendisligi Bolumu, Mustafa Kemal Univ., Hatay, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

2513

Lastpage

2516

Abstract

In this study, a large number of features that were obtained to classify speech emotions were projected into different spaces, selecting different numbers of principal components in principal component analysis and Fisher´s discriminant analysis. Classifications were performed in those spaces using Naïve-Bayes classifier and obtained results were compared. While the highest accuracy obtained in the Fisher space was 57.87%, it was calculated as 48.02% in the principal component space.

Keywords

Bayes methods; emotion recognition; principal component analysis; signal classification; speech recognition; Fisher discriminant analysis; Fisher space; emotion recognition; naïve-Bayes classifier; principal component analysis; principal component space; speech emotion classification; Emotion recognition; Feature extraction; Linear discriminant analysis; Pattern analysis; Principal component analysis; Signal processing; Speech; emotion recognition; fisher´s linear discriminant analysis; principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7130395

Filename

7130395