Comparison of perceptual features efficiency for automatic identification of emotional states from speech

Author

Kaminska, D. ; Sapinski, T. ; Pelikant, A.

Author_Institution

Lodz Univ. of Technol., Lodz, Poland

fYear

2013

fDate

6-8 June 2013

Firstpage

210

Lastpage

213

Abstract

The following paper presents parameterization of emotional speech using perceptual coefficients as well as a comparison of Mel Frequency Cepstral Coefficients (MFCC), Bark Frequency Cepstral Coefficients (BFCC), Perceptual Linear Prediction Coefficients (PLP) and Revised Perceptual Linear Prediction Coefficients (RPLP). Analysis was performed on two different databases: Database of Polish Emotional Speech and the most commonly used for emotion recognition - Berlin Database of Emotional Speech. Both consist of acted emotional speech grouped into six classes of primary emotions. Emotion classification was performed using k-NN algorithm.

Keywords

cepstral analysis; emotion recognition; feature extraction; pattern classification; speech recognition; BFCC; Bark frequency cepstral coefficients; Berlin Database of Emotional Speech; Database of Polish Emotional Speech; MFCC; Mel frequency cepstral coefficients; PLP coefficients; RPLP coefficients; automatic emotional state identification; emotion classification; emotion recognition; emotional speech parameterization; k-NN algorithm; perceptual feature efficiency; perceptual linear prediction coefficients; primary emotions; revised perceptual linear prediction coefficients; Databases; Emotion recognition; Feature extraction; Mel frequency cepstral coefficient; Speech; Speech recognition; emotion recognition; perceptual coefficients; speech signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Human System Interaction (HSI), 2013 The 6th International Conference on

Conference_Location

Sopot

ISSN

2158-2246

Print_ISBN

978-1-4673-5635-0

Type

conf

DOI

10.1109/HSI.2013.6577824

Filename

6577824