Emotion recognition in speech using inter-sentence Glottal statistics

This study deals with the recognition of three emotional states in speech, namely: happiness, anger, and sadness. The corpus included speech from six subjects (3M and 3F) speaking ten sentences. Glottal inverse filtering was first performed on the spoken utterances. Then parameters for computing the glottal symmetry were collected and computed to create a final matrix of features. A combined with all emotions across the different subjects was formed and used to train a Gaussian mixture model (GMM) classifier. Training on 80% of all combined utterances for each emotion was performed. Testing was administered on the remaining 20%. The system shows confidence that glottal information may be used for determining the correct emotion in speech. The recognition performance varied between 48.96% and 82.29%.