Speech Emotion Recognition Using an Enhanced Kernel Isomap for Human-Robot Interaction
نویسندگان
چکیده
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 ʹenhanced kernel isometric mappingʹ (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
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