Speech Emotion Recognition Using an Enhanced Kernel Isomap for Human-Robot Interaction. (11th February 2013)

• Record Type: Journal Article
• Authors: Zhang, Shiqing; Zhao, Xiaoming; Lei, Bicheng
• Is Part Of: International journal of advanced robotic systems. Volume 10:Number 2(2013)
• Publication Date: 2013-02-11
• Subjects: Speech Emotion Recognition -- Nonlinear Dimensionality Reduction -- Human-Robot Interaction; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/55403
• Languages: English
• ISSNs: 1729-8806

Abstract

Speech emotion recognition is currently an active research subject and has attracted extensive interest in the science community due to its vital application to human-robot interaction. Most speech emotion recognition systems employ high-dimensional speech features, indicating human emotion expression, to improve emotion recognition performance. To effectively reduce the size of speech features, in this paper, a new nonlinear dimensionality reduction method, called 'enhanced kernel isometric mapping' (EKIsomap), is proposed and applied for speech emotion recognition in human-robot interaction. The proposed method is used to nonlinearly extract the low-dimensional discriminating embedded data representations from the original high-dimensional speech features with a striking improvement of performance on the speech emotion recognition tasks. Experimental results on the popular Berlin emotional speech corpus demonstrate the effectiveness of the proposed method.