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Application of dimensional emotion model in automatic emotional speech recognition

This paper reports on the application of the dimensional emotion model in automatic emotional speech recognition. Using the perceptron rule in combination with acoustic features, an approach to speech-based emotion recognition is introduced, which can classify the utterance with respect to the valence-arousal (V-A) dimensions of its emotional content. The mapping of 5 discrete emotion classes onto the 3-class emotional clusters in the V-A space was adopted. Two corpora of acted emotional speech were used to compare recognition results: the Berlin Emotional Speech Database (in German) and the Corpus of Emotional and Attitude Expressive Speech (in Serbian). The experimental results show that the discrimination of emotional speech along the arousal dimension is better than the discrimination along the valence dimension for both corpora.