Abstract

In this paper, we consider both speaker dependent and listener dependent aspects in the assessment of emotions in speech. We model the speaker dependencies in emotional speech production by two parameters which describe the individual’s emotional expression behavior. Similarly, we model the listener’s emotion perception behavior by a simple parametric model. These models form a basis for improving current automatic emotion recognition schemes such as, for example, for manmachine interaction applications. For this task, an emotional speech database of the four emotion categories angry, happy, neutral, and sad was evaluated by 18 human listeners. For each of the 680 sentences, the evaluators rated the values of three emotion primitives, valence, activation, and dominance, each on a 5-point scale. The assessment results were used to calculate the distributions (centroids, covariances) of the emotion classes in the space spanned by the three emotion primitives. The individual classes formed separable clusters in the emotion space. Based on these results, we analyzed the variations of the emotion clusters as a function of speaker and listener.

Date

2006

Authors

Michael Grimm, Kristian Kroschel, Shrikanth Narayanan

Journal

Proc. ISCA 3rd Internat. Conf. on Speech Prosody, Dresden, Germany

Pages

9-12