基于类依赖的语音情感特征选择

陈俊; 王爱国; 王坤侠; 安宁; 李廉

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基于类依赖的语音情感特征选择

更新时间：2026-09-04

- 基于类依赖的语音情感特征选择
- Speech Emotion Recognition with Class-dependent Feature Selection Methods
- 微电子学与计算机 2016年33卷第8期页码：92-96
- 作者机构：
  
  1. 合肥工业大学计算机与信息学院,安徽,合肥,230009
  2. 安徽建筑大学机构电子与信息工程学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61305064国家高等学校学科创新引智计划("111") B14025国家科技支撑计划 2013BAH19F01教育部直属高校外国文教专家年度聘请计划-学校特色项目 TS2013HFGY031
- DOI：
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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陈俊, 王爱国, 王坤侠, 安宁, 李廉. 基于类依赖的语音情感特征选择[J]. 微电子学与计算机, 2016, 33(8): 92-96, 101.

CHEN Jun, WANG Ai-guo, WANG Kun-xia, et al. Speech Emotion Recognition with Class-dependent Feature Selection Methods[J]. 2016, 33(8): 92-96.
陈俊, 王爱国, 王坤侠, 安宁, 李廉. 基于类依赖的语音情感特征选择[J]. 微电子学与计算机, 2016, 33(8): 92-96, 101. DOI：

CHEN Jun, WANG Ai-guo, WANG Kun-xia, et al. Speech Emotion Recognition with Class-dependent Feature Selection Methods[J]. 2016, 33(8): 92-96. DOI：

摘要

提出一种基于类依赖的语音情感特征选择模型

该模型可以提高语音情感识别的准确率.应用马尔科夫毯分别为每类情感筛选出具有较高表征能力的特征子集

通过支持向量机用于学习情感分类.为了更好地融合多分类器的分类决策

将支持向量机的二分类转化为概率分类

以解决多分类器联合使用的投票冲突问题.在公开数据集上的实验结果表明

与信息增益、主成分分析以及类独立的特征选择方法相比

此方法可以较大幅度减少样例维数

同时获得较高的情感识别率.

Abstract

This study proposes a novel class-dependent feature selection model to improve the performance of speech emotion recognition. Particularly

we adopt the markov blanket technique to select discriminative features for each emotion class

and use the support vector machine classifier to build the emotion recognition model. To make better decision in multi-label classification

the binary discriminative output of the support vector machine is transformed to a probability output for solving the voting conflict problem. Extensive experimental results on the a publicly available dataset show that in comparison with information gain

principal component analysis

and class-independent feature selectors

the proposed method significantly reduces the feature dimensionality and obtains better classification accuracy.

关键词

Keywords

references

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Xie B, Chen L, Chen G C, et al. Feature selection for emotion recognition of mandarin speech [J]. Journal of Zhejiang University Engineering Science, 2007, 41(11): 1816-1822. DOI: 1816

Wang Ai-guo, An Ning, Chen Gui-lin, et al. Accelerating wrapper-based feature selection with K-nearest-neighbor [J]. Knowledge-Based Systems, 2015, 83(1): 81-91. DOI: 81

Koller D, Sahami M. T, Koller D, et al. Toward Optimal Feature Selection [J]. International Conference on Machine Learning, 1996, 20(3): 284-292. DOI: 284

Chang C C, Lin C J. LIBSVM: a Library for Support Vector Machines [J]. ACM Transactions on Intelligent Systems % Technology, 2006, 2(3): 389-396. DOI: 389

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