Detection of Emotional Expressions in Speech

Author

Julia, Fatema N. ; Iftekharuddin, Khan M.

Author_Institution

Dept. of Electr. & Comput. Eng., Memphis Univ.

fYear

2005

fDate

March 31 2005-April 2 2005

Firstpage

307

Lastpage

312

Abstract

This paper provides a survey literature survey on the emotion recognition in spoken dialogs and proposes an implementation of such a system using acoustic features. The data corpus contains 322 utterances expressing four emotions such as happy, angry, sad, and fear. 50% of the total data is used for training while the other 50% is used for testing. We use 21 features extracted from our features set in our experiment. The feature vectors are normalized by using Z-score normalization. The multi-class support vector machine (SVM) classifier is used for classification. The result shows that sad is classified with the highest accuracy whereas happy is classified with the least accuracy

Keywords

emotion recognition; feature extraction; speech recognition; support vector machines; Z-score normalization; classification; emotion recognition; emotional expressions detection; feature extraction; multiclass support vector machine; speech; spoken dialogs; Acoustic signal detection; Emotion recognition; Feature extraction; Frequency; Humans; Psychology; Signal analysis; Speech analysis; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

SoutheastCon, 2006. Proceedings of the IEEE

Conference_Location

Memphis, TN

Print_ISBN

1-4244-0168-2

Type

conf

DOI

10.1109/second.2006.1629369

Filename

1629369