Speech Emotion Recognition: Comparison of Speech Segmentation Approaches

Recognition of emotional states carried in speech, is of a great interest in modern human computer interaction developments. To reliably detect the aroused emotion, a sufficiently long continuous speech segment is required. This research aims to analyze different segmentation approaches of speech signals. Berlin emotional speech database is used for data set generation. Time frame based and voiced segmentation approaches are applied and compared. The experimental results show that accurate emotion recognition is obtained when the speech segments are longer than a second or are composed of 10 to 12 voiced segments. Based on the findings of this research, voiced based segmentation generates more accurate results than the other methods

Muharram Mansoorizadeh Tarbiat Modarres University

Key research interests: Facial Emotion Recognition Multi-modal Emotion Recognition

mansoorm_IKT07_speech_emotion_recognition.pdf — PDF document, 75Kb