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General information |
| Author |
Zhang, Shiqing; Zhao, Xiaoming; Lei, Bicheng |
| Published |
InTech Open Access Publisher, 2013
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| 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
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 highdimensional
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. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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