计算机与数字工程2019，Vol.47Issue(3)：539-542,4.DOI:10.3969/j.issn.1672-9722.2019.03.011
基于随机森林的语音情感特征选择与分类
Speech Emotion Feature Selection and Classification Based on Random Forest
邢尹 1刘立龙1
作者信息
- 1. 桂林理工大学测绘地理信息学院 桂林 541004
- 折叠
摘要
Abstract
Aiming at complex and high-dimensional speech emotion feature,selecting the key features shows great signifi?cance to reduce the complexity and improve the performance of the model. In this work,a fused feature selection method,based on the Fisher criterion and mean decrease Gini index in random forest is proposed. Firstly,the Fisher criterion and mean decrease Gini index evaluate the importance of all the features respectively. Secondly,a certain threshold is set to select preferable features,carry?ing out the intersection operation. Thirdly,the features are rearranged according to the order of the mean decrease Gini index. Final?ly,the optimal feature dimensions are determined by the recognition rate on validation set. The experimental results show that the fused feature selection method is effective and can improve the rate.关键词
特征选择/随机森林/Fisher准则/Gini指数/语音情感识别Key words
feature selection/random forest/Fisher criterion/Gini index/speech emotion recognition分类
信息技术与安全科学引用本文复制引用
邢尹,刘立龙..基于随机森林的语音情感特征选择与分类[J].计算机与数字工程,2019,47(3):539-542,4.
