2003/09/01 by Sherif Yacoub, Steve Simske, Xiaofan Lin +1 · 1 citation
Psychology · Computer Science · #Emotion and Mood Recognition #Speech Recognition and Synthesis #Social Robot Interaction and HRI #Sadness #Boredom #Anger #Speech recognition #Focus (optics) #Computer science #Support vector machine #Salient #Emotion classification #Artificial intelligence #Natural language processing #Psychology #Social psychology
paper · doi:10.21437/eurospeech.2003-307
openalex publication_date 2003/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
This paper reports emotion recognition results from speech signals, with particular focus on extracting emotion features from the short utterances typical of Interactive Voice Response (IVR) applications. We focus on distinguishing anger versus neutral speech, which is salient to call center applications. We report on classification of other types of emotions such as sadness, boredom, happy, and cold anger. We compare results from using neural networks, Support Vector Machines (SVM), K-Nearest Neighbors, and decision trees. We use a database from the Linguistic Data Consortium at University of Pennsylvania, which is recorded by 8 actors expressing 15 emotions. Results indicate that hot anger and neutral utterances can be distinguished with over 90% accuracy. We show results from recognizing other emotions. We also illustrate which emotions can be clustered together using the selected prosodic features.