Comparing Feature Sets for Acted and Spontaneous Speech in View of Automatic Emotion Recognition

Thurid Vogt and Elisabeth André

erschienen 2005 "IEEE International Conference on Multimedia & Expo (ICME 2005)", Pages 474 - 477

Verlag: IEEE Computer Society

ISBN: 0-7803-9331-7 DOI: IEEE Computer Society


We present a data-mining experiment on feature selection for automatic emotion recognition. Starting from more than 1000 features derived from pitch, energy and MFCC time series, the most relevant features in respect to the data are selected from this set by removing correlated features. The features selected for acted and realistic emotions are analysed and show significant differences. All features are computed automatically and we also contrast automatically with manually units of analysis. A higher degree of automation did not prove to be a disadvantage in terms of recognition accuracy.


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