基于小波散射变换和MFCC的双特征语音情感识别融合算法

应娜; 吴顺朋; 杨萌; 邹雨鉴

doi:10.11959/j.issn.1000-0801.2024088

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基于小波散射变换和MFCC的双特征语音情感识别融合算法

研究与开发 | 更新时间：2024-08-05

- 基于小波散射变换和MFCC的双特征语音情感识别融合算法
- Dual-feature speech emotion recognition fusion algorithm based on wavelet scattering transform and MFCC
- 电信科学 2024年40卷第5期页码：62-72
- 作者机构：
  
  杭州电子科技大学通信工程学院，浙江杭州 310018
- 作者简介：
  
  [ "应娜（1978- ），女，博士，杭州电子科技大学通信工程学院副教授、硕士生导师，主要研究方向为智能信号处理与应用。" ]
  [ "吴顺朋（1994- ），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为语音信号处理。" ]
  [ "杨萌（1980- ），男，杭州电子科技大学通信工程学院副教授、硕士生导师，主要研究方向为SAR图像目标检测与识别及大模型应用。" ]
  [ "邹雨鉴（1998- ），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为语音信号处理。" ]
- 基金信息：
  
  浙江省自然科学基金资助项目(LTGS23F010001);浙江省属高校基本科研业务费专项资金资助项目(GK239909299001-406)
- DOI：10.11959/j.issn.1000-0801.2024088
  中图分类号： TP18
- 收稿日期：2023-11-30，
  
  修回日期：2024-04-10，
  
  纸质出版日期：2024-05-20
- 稿件说明：
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应娜,吴顺朋,杨萌等.基于小波散射变换和MFCC的双特征语音情感识别融合算法[J].电信科学,2024,40(05):62-72.

YING Na,WU Shunpeng,YANG Meng,et al.Dual-feature speech emotion recognition fusion algorithm based on wavelet scattering transform and MFCC[J].Telecommunications Science,2024,40(05):62-72.
应娜,吴顺朋,杨萌等.基于小波散射变换和MFCC的双特征语音情感识别融合算法[J].电信科学,2024,40(05):62-72. DOI： 10.11959/j.issn.1000-0801.2024088.

YING Na,WU Shunpeng,YANG Meng,et al.Dual-feature speech emotion recognition fusion algorithm based on wavelet scattering transform and MFCC[J].Telecommunications Science,2024,40(05):62-72. DOI： 10.11959/j.issn.1000-0801.2024088.

摘要

为了充分挖掘语音信号频谱包含的情感信息以提高语音情感识别的准确性，提出了一种基于小波散射变换和梅尔频率倒谱系数（Mel-frequency cepstral coefficient，MFCC）的排列熵加权和偏差调整规则的语音情感识别融合算法（PEW-BAR）。算法首先获取语音信号的小波散射特征和梅尔频率倒谱系数的相关特征；然后按尺度维度扩展小波散射特征，利用支持向量机得到情感识别的后验概率并获得排列熵，并使用排列熵对后验概率进行加权；最后采用一种偏差调整规则进一步融合MFCC的相关特征的识别结果。实验结果表明，在EMODB、RAVDESS和eNTERFACE05数据集上，与传统的基于小波散射系数的语音情感识别方法相比，该算法将ACC分别提高了2.82%、2.85%和5.92%，将UAR分别提升了3.40%、2.87%和5.80%，IEMOCAP上提高了6.89%。

Abstract

A fusion algorithm named permutation entropy weighted and bias adjustment rule fusion (PEW-BAR) was proposed to enhance the accuracy of speech emotion recognition by exploiting the emotional information in the spectral characteristics of speech signals. The algorithm was based on the integration of wavelet scattering transform and Mel-frequency cepstral coefficients (MFCC). Firstly

wavelet scattering features and MFCC-related features from speech signals were extracted. Then

the wavelet scattering features were expanded in the scale dimension and applied support vector machines to obtain posterior probabilities for emotion recognition. And permutation entropy was calculated and a weighted fusion based on this entropy was subsequently applied. Finally

a bias adjustment rule was utilized to refine the integration results obtained from the MFCC-related features. Experimental results on various datasets

including EMODB

RAVDESS

and eNTERFACE05

demonstrate notable improvements. The proposed algorithm outperforms traditional wavelet scattering coefficient-based methods

achieving accuracy improvements of 2.82%

2.85%

and 5.92%

respectively. Additionally

it shows enhancements of 3.40%

2.87%

and 5.80% in terms of unweighted average recall (UAR)

and a 6.89% improvement on the IEMOCAP dataset.

关键词

Keywords

references

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