Speech Emotion Recognition Using an Enhanced Kernel Isomap for Human-Robot Interaction

• Published in: International journal of advanced robotic systems Vol. 10; no. 2
• Main Authors: Zhang, Shiqing, Zhao, Xiaoming, Lei, Bicheng
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 11.02.2013; Sage Publications Ltd; SAGE Publishing
• Subjects: Accuracy; Acoustics; Classification; Data processing; Emotion recognition; Emotions; Feature recognition; Human engineering; Kernels; Mapping; Principal components analysis; Robots; Speech recognition; Teaching methods; Human-Robot Interaction; Speech Emotion Recognition; Nonlinear Dimensionality Reduction
• ISSN: 1729-8806; 1729-8814
• DOI: 10.5772/55403

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.