Hybrid NaIve Bayes K-Nearest Neighbor Method Implementation on Speech Emotion Recognition

被引：0

作者：

Leo, Seho ^{[1
]}

机构：

[1] Hankuk Acad Foreign Studies, Dept Int Studies, Yongin, South Korea

来源：

2015 IEEE ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) | 2015年

关键词：

Naive Bayes; K-Nearest Neighbors; Speech emotion recognition; SUPPORT VECTOR MACHINE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Speech Emotion Recognition technique is incredible in that it can open a way of communication between human and computer. The applications vary from educational software, psychiatric diagnosis, and interrogation to intelligent toys. It has been a long way for researchers who dedicated to search for the best models for speech emotion recognition. This paper proposes a novel hybrid model that combines the K Nearest Neighbor (KNN) model and the Naive Bayes (NB) classifier: a model which was inspired from the hybrid model of Support Vector Machine (SVM) and K-Nearest Neighbor method. The implementation of NB-KNN overcomes risks of SVM-KNN model and outperforms the original models that it is composed of.

引用

页码：349 / 353

页数：5

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