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.

Abstract. We present EmoVoice, a framework for emotional speech corpus and classifier creation and for offline as well as real-time online speech emotion recognition. The framework is intended to be used by non-experts and therefore comes with an interface to create an own personal or application specific emotion recogniser. Furthermore, we describe some applications and prototypes that already use our framework to track online emotional user states from voice information.

In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states -confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of 'most important' features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.

Abstract. In this article we give guidelines on how to address the major technical challenges of automatic emotion recognition from speech in human-computer interfaces, which include audio segmentation to find appropriate units for emotions, extraction of emotion relevant features, classification of emotions, and training databases with emotional speech. Research so far has mostly dealt with offline evaluation of vocal emotions, and online processing has hardly been addressed. Online processing is, however, a necessary prerequisite for the realization of human-computer interfaces that analyze and respond to the user's emotions while he or she is interacting with an application. By means of a sample application, we demonstrate how the challenges arising from online processing may be solved. The overall objective of the paper is to help readers to assess the feasibility of human-computer interfaces that are sensitive to the user's emotional voice and to provide them with guidelines of how to technically realize such interfaces.