Speech activity detection and automatic prosodic processing unit segmentation for emotion recognition

In speech communication emotions play a great role in expressing information. These emotions are partly given as reactions to our environment, to our partners during a conversation. Understanding these reactions and recognizing them automatically is highly important. Through them, we can get a clearer picture of the response of our partner in a conversation. In Cognitive Info Communication this kind of information helps us to develop robots, devices that are more aware of the need of the user, making the device easy and enjoyable to use. In our laboratory we conducted automatic emotion classification and speech segmentation experiments. In order to develop an automatic emotion recognition system on the basis of speech, an automatic speech segmenter is also needed to separate the speech segments needed for the emotion analysis. In our former research we found that the intonational phrase can be a proper unit of emotion analysis. In this paper speech detection and segmentation methods are developed. For speech detection, Hidden Markov Models are used with various noise and speech acoustic models. The results show that the procedure is able to detect speech in the sound signal with more than 91% accuracy and segment it into intonational phrases.