Characterization of Audiovisual Dramatic Attitudes

In this work we explore the capability of audiovisual parameters (such as voice frequency, rhythm, head motion or facial expressions) to discriminate among different dramatic attitudes. We extract the audiovisual parameters from an acted corpus of attitudes and structure them as frame, syllable, and sentence-level features. Using Linear Discriminant Analysis classifiers, we show that sentence-level features present a higher discriminating rate among the attitudes and are less dependent on the speaker than frame and sylable features. We also compare the classification results with the perceptual evaluation tests, showing that voice frequency is correlated to the perceptual results for all attitudes, while other features, such as head motion, contribute differently, depending both on the attitude and the speaker.