期刊文献+

语音情感识别技术在心理辅导中的研究和应用

Research and Application of Speech Emotion Recognition Technology in Psychological Counseling
下载PDF
导出
摘要 语音情感识别是用计算机去识别输入语音信号的特征,计算分析得出情感状态。文章建立的语音情感识别系统,用支持向量机SVM去识别输入的语音模型,对输入的语音信号进行预加重处理、端点检测、mfcc情感特征提取,与已训练语音提取的情感特征进行一定的特征比对,分析情感状态。通过对语音数据库模型的训练和测试,针对高兴、悲伤、愤怒等三种情感,最终对语音数据库里的语料达到了71.3%的识别率。最后根据识别的情绪进一步进行相关的心理辅导。 Speech emotion recognition is to use the computer to recognize the features of the input speech signal, and calculate and analyze the emotional state. In the speech emotion recognition system built in this paper, SVM is used to recognize the input speech model. The system performs preemphasis processing, endpoint detection, MFCC emoion feature extraction on the input speech signal, and compares the emotion feature with the emotion feaure extracted by the trained speech to get the existing emotion.Through the training and testing of the speech database mod-el, aiming at the three emotions of happiness, sadness and anger, the recognition rate of the corpus in the speech database reaches 71.3%. And according to the identified emotions for further psychological counseling.
作者 史国燕 樊思滢 王乐 SHI Guoyan;FAN Siying;WANG Le(Xi'an University of Architecture and Technology,College of Information and Control Engineering,Shanxi xi,an 710304,China)
出处 《长江信息通信》 2022年第9期19-22,共4页 Changjiang Information & Communications
基金 省级大学生创新创业训练计划项目“语音情感识别技术在心理辅导中的研究和应用”,项目编号为S202110703128。
关键词 语音情感识别 支持向量机 心理辅导 语音数据库 MFCC Speech Emotion Recognition Support Vector Machine Psychological Counseling Voice Database MFCC
  • 相关文献

参考文献2

二级参考文献19

  • 1MALLAT S. A theory for mulitiresolution signal decomposition:The wavelet representation [J]. IEEE Trans. PAMI, 1989,11(7) : 647-693.
  • 2MALLAT S. Multifrequency channel decompositions of images and wavelets models [J]. IEEE Trans.ASSP, 1989,37(12) : 2091-2110.
  • 3MALLAT S. Zero-crossing of a wavelet transform[J]. IEEE Trans. On IT, 1991,37(7): 1019-1033.
  • 4MALLAT S, HUANG W L. Singularity detection and processing with wavelets [J]. IEEE Trans. On IT,1992,41(9) :710-732.
  • 5VAPNIK V. The Nature of Statistical Learning Theory[M]. Springer, 1995.
  • 6CHAPELLE O, HAFFNER P, VAPNIK V. Support vector machines for histogram-based image classification[J]. IEEE Trans. on Neural Networks, 1999, 10(5):1055-1064.
  • 7AMBRUS D C. Collecting and recording of an emotional speech Database[D].Maribor,Slovenia:Faculty of Electrical Engineering and Computer Science,Institute of Electronics,University of Maribor,2000.
  • 8MCGILLOWAY S,COWIE R,DOUGLAS-COWIE. Approaching automatic recognition of emotion from voice:a rough benchmark[A].Belfast,UK:ISCA,2000.207-212.
  • 9DOUGLAS-COWIE E,COWIE R,der M S. A new emotion database:considerations,sources and scope[A].Belfast,UK:ISCA,2000.39-44.
  • 10BURKHARDT F,PAESCHKE A,ROLFES M. A database of German emotional speech[A].Lisbon,Portugal:ISCA,2005.1517-1520.

共引文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部