Optimal prototype selection for speech emotion recognition using fuzzy k-important nearest neighbour
- Authors
- Zhang, Zhen Xing; Lim, Joon Shik; Jiang, Zhao Cai; Zhou, Chun Jie; Li, Shao Jing
- Issue Date
- 2016
- Publisher
- INDERSCIENCE ENTERPRISES LTD
- Keywords
- speech emotion recognition; prototype selection; nearest neighbour
- Citation
- INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS, v.17, no.2, pp.103 - 119
- Journal Title
- INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS
- Volume
- 17
- Number
- 2
- Start Page
- 103
- End Page
- 119
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/9764
- DOI
- 10.1504/IJCNDS.2016.079096
- ISSN
- 1754-3916
- Abstract
- Speech emotion recognition has been a popular topic of affective computing. Accuracy in speech emotion recognition depends on selecting the optimal prototype. In this paper, a new 2-D emotional speech recognition model based on a fuzzy k-important nearest neighbour (FKINN) and neuro-fuzzy network is described. In the FKINN algorithm, an important nearest neighbour selection rule is introduced. The neuro-fuzzy network applies a bounded sum of weighted fuzzy membership functions (BSWFM). During the training process, BSWFM calculates the Takagi-Sugeno defuzzification values for the 2-D visual model. The emotional speech signals used in this work were obtained from the Berlin emotional speech database. The proposed new model achieves 83.5% overall classification accuracy with the 2-D emotional speech recognition model. The classification accuracies of anger, happiness, sadness, and neutral were 94.1%, 65.9%, 81.1%, and 87.5%, respectively.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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