Emotional Speech Recognition Based on PAD Emotion Model

SONG Jing; ZHANG Xue-ying; SUN Ying; ZHANG Wei

Chinese

您当前的位置：

首页 >

文章列表页 >

Emotional Speech Recognition Based on PAD Emotion Model

更新时间：2026-09-04

- Emotional Speech Recognition Based on PAD Emotion Model
- Vol. 33, Issue 9, Pages: 128-131(2016)
- 作者机构：
  
  太原理工大学信息与工程学院,山西,太原,030024
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
移动端阅览
宋静, 张雪英, 孙颖, 张卫. 基于PAD情绪模型的情感语音识别[J]. 微电子学与计算机, 2016, 33(9): 128-131,136.

SONG Jing, ZHANG Xue-ying, SUN Ying, et al. Emotional Speech Recognition Based on PAD Emotion Model[J]. 2016, 33(9): 128-131.
宋静, 张雪英, 孙颖, 张卫. 基于PAD情绪模型的情感语音识别[J]. 微电子学与计算机, 2016, 33(9): 128-131,136. DOI：

SONG Jing, ZHANG Xue-ying, SUN Ying, et al. Emotional Speech Recognition Based on PAD Emotion Model[J]. 2016, 33(9): 128-131. DOI：

摘要

简述梅尔频率倒谱系数、线性预测系数、韵律学特征、共振峰频率和过零峰值幅度特征

并将这五种语音特征应用于情感语音识别.根据识别结果从PAD情绪模型的三个维度进行相关性分析得到特征的权重系数

并将识别结果融合映射到PAD三维情绪空间

最终获得情感语音的PAD值.利用情感语音的PAD值可以从连续情感理论对情感语音进行描述分析

采用量化的方法揭示情感空间中各种情绪范畴的定位和关系.

Abstract

Five approaches of feature extraction: the MEL-frequency Cepstral Coefficient(MFCC)

the Linear Predictor Coefficient(LPC)

prosodic features

formant frequency and the Zero Crossings with Peak Amplitudes(ZCPA)are described in this paper.These features are applied to emotional speech recognition.According to the recognition results

the weight coefficients of features are obtained by correlation analysis in the three dimensions of PAD emotion model.Simultaneously the recognition results are fused to the PAD emotional space

and the PAD values of the emotional speech are obtained.The PAD values of the emotional speech can be analyzed from the theory of continuous emotion.And the quantitative analysis of emotional speech can reveal the position and relationship of emotional category in emotional space.

关键词

Keywords

references

Backs R W, da Silva S P, Han K. A comparison of younger and older adults'self-assessment manikin ratings of affective pictures[J]. Experimental Aging Research, 2005, 31(4): 421-440. DOI: 421

Deshmukh A A, Pramod K M. Combined LPC and MFCC features based technique for isolated speech recognition[C]//Proc of ISCAS 2013. Beijing: IEEE, 2013: 437-441.

Caglar Oflazoglu, Serdar Yildirim. Recognizing emotion from turkish speech using acoustic features[J]. EURASIP Journal on Audio, Speech and Music Processin, 2013, 20013(26): 1-11. DOI: 1

孙慧. 粒子群优化模糊神经网络用于语音识别的研究[D]. 太原: 太原理工大学, 2010.

Ying Sun, Xueying Zhang. A study of zero-crossings with peak-amplitudes in speech emotion classification[C]//The First International Conference on Pervasive Computing, Signal Processing and Applications. Harbin, China: IEEE, 2010: 328-331.

张雪英. 数字语音处理及Matlab仿真[M]. 北京: 电子工业出版社, 2010: 1-9. DOI: 1

Mehrabian A. Pleasure-arousal-dominance: ageneral framework for describing and measuring individual differences in Temperament[J]. Current Psychology, 1996, 14(4): 261-292. DOI: 261

吴炜烨. 基于神经网络语音识别算法的研究[D]. 长沙: 中南大学, 2009.

Li X, Zhou H, Song S, et al. The reliability and validity of the chinese version of abbreviated PAD emotion scales[C]//Proceedings of the International Conference on Affective Computing and Intelligent Interaction. Beijing: IEEE, 2005: 513-518.

韩文静, 李海峰, 阮华斌, 等. 语音情感识别研究进展综述[J]. 软件学报, 2014, 25(1): 37-50. DOI: 37

孙颖, 张雪英. 情感语音特征对语料库依赖性的统计分析[J]. 噪声与振动控制, 2011, 31(4): 132-136. DOI: 132

赵力, 黄程韦. 实用语音情感识别中的若干关键技术[J]. 数据采集与处理, 2014, 29(2): 157-170. DOI: 157

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design and Implementation of Neural Correlation Analysis Based on FPGA

Steganography model guided by attention mechanism and improved dense convolution

Research on the design of blockchain-based AI-generated content traceability framework

High gain stability high temperature operational amplifier based on reverse body bias

Aerial small object detection algorithm based on improved RT-DETR

Related Author

LIU Pei-long

HUANG Le-tian

LIN Ling-peng

Related Institution

School of Communication and Information Engineering of UESTC

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰