Automatic Recognition of Spontaneous Emotions in Speech Using Acoustic and Lexical Features

We developed acoustic and lexical classifiers, based on a boosting algorithm, to assess the separability oil arousal and valence dimensions in spontaneous emotional speech. The spontaneous emotional speech data was acquired by inviting subjects to play a first-person shooter video game. Our acoustic classifiers performed significantly better than the lexical classifiers on the arousal dimension. On the valence dimension, our lexical classifiers usually outperformed the acoustic classifiers. Finally, fusion between acoustic and lexical features on feature level did not always significantly, improve classification performance.