Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine

LIU Ming-zhu; LI Xiao-qin; CHEN Hong-heng

doi:10.15938/j.jhust.2019.04.020

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Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine

更新时间：2026-08-31

- Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine
- Vol. 24, Issue 4, Pages: 118-126(2019)
- 作者机构：
  
  哈尔滨理工大学测控技术与通信工程学院,黑龙江,哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.15938/j.jhust.2019.04.020
  CLC： TN912.34
- Published：2019
- 稿件说明：
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LIU Ming-zhu, LI Xiao-qin, CHEN Hong-heng. Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine[J]. 2019, 24(4): 118-126.
DOI：

LIU Ming-zhu, LI Xiao-qin, CHEN Hong-heng. Research on Speech and Emotional Recognition Algorithm Based on Support Vector Machine[J]. 2019, 24(4): 118-126. DOI： 10.15938/j.jhust.2019.04.020.

摘要

为了提高语音情感识别系统的识别率，研究分析了一种支持向量机核函数参数的优选方法。首先给出影响支持向量机核参数的因素，其次依据这些因素，结合Fisher准则和最大熵原理对支持向量机的核参数进行优选。最后用优选参数对基于情感语音数据库进行5种情感的识别测试，测试结果表明Fisher准则和最大熵方法相融合能够有效地提高语音情感识别准确率。

Abstract

In order to improve the recognition rate of speech emotion recognition system

this paper studies a preferred method of supporting vector machine kernel function parameters. Firstly

according to the principle of support vector machine

the factors influencing the kernel parameters are given.Secondly

according to these factors

the kernel parameters of the support vector machine are optimized according to the Fisher criterion and the maximum entropy principle. Finally

five kinds of emotion recognition tests based on the emotional speech database are selected with the preferred parameters. The test results show that the fusion of the Fisher criterion and the maximum entropy method can effectively improve the accuracy of speech emotion recognition.

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Related Institution

1School of Information and Computer Engineering, Northeast Forestry University

2State Forestry Administration, Harbin Forestry Machinery Research Institute

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