Detecting Practical Speech Emotion in a Cognitive Task

In this paper we analysis the speech emotions related to cognitive process. An automatic system is established for detecting speech emotions including anxiety, hesitation, confidence and joy. In order to obtain a naturalistic database we use noise to induce negative emotions, sleep deprivation is also used for this purpose. The lack of sleep is an important cause for anxiety. Annotation of emotional speech is then done manually with a self evaluation for the felt emotions in each utterance. Acoustic features are extracted both for valence dimension and arousal dimension including voice quality features. For the recognition algorithm Gaussian Mixture Model is adopted for detecting each type of emotions from neutral speech. Based on the detection of each emotion in the continuous recognition of emotion states an error correcting method is proposed. With the previous emotion states and cognitive performance the detection errors in current emotion state is corrected with empirical probability. Experimental results show that our system can detect "practical" speech emotions related to cognitive process. With the proposed error correcting method the recognition performance is improved compared to the baseline system based on Gaussian Mixture Model. We believe the detection of these "practical" emotions is important for real world applications, especially for helping people cope with negative emotions in cognitive activities.