A study on automatic recognition of positive and negative emotions in speech

Author

Pavaloi, I. ; Ciobanu, Amelia ; Luca, Mihaela ; Musca, E. ; Barbu, Tudor ; Ignat, Anca

Author_Institution

Inst. of Comput. Sci., Iasi, Romania

fYear

2014

fDate

17-19 Oct. 2014

Firstpage

221

Lastpage

224

Abstract

The paper is focused on an experimental study on positive and negative emotion vocal recognition. After some considerations about the positive and negative emotions, the paper gives a short description of the three corpuses used in the work we have accomplished. The paper describes three sets of coefficients used, the statistic features used to generate the three sets of feature vectors and the two classification methods used in this study. The recognition results obtained for every corpus are shown and some conclusions and directions of development are presented.

Keywords

emotion recognition; signal classification; speech processing; speech recognition; statistical analysis; automatic negative speech emotion recognition; automatic positive speech emotion recognition; classification methods; feature vector generation; negative emotion vocal recognition; positive emotion vocal recognition; statistic features; Classification algorithms; Emotion recognition; Mel frequency cepstral coefficient; Speech; Speech recognition; Support vector machine classification; SVM; k-NN; pattern classification; positive and negative emotion recognition; speech processing; speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, Control and Computing (ICSTCC), 2014 18th International Conference

Conference_Location

Sinaia

Type

conf

DOI

10.1109/ICSTCC.2014.6982419

Filename

6982419