厌恶和无聊状态的情绪识别

S. M. Feraru, M. Zbancioc
{"title":"厌恶和无聊状态的情绪识别","authors":"S. M. Feraru, M. Zbancioc","doi":"10.1109/ISSCS.2017.8034913","DOIUrl":null,"url":null,"abstract":"In this paper, we made the emotion recognition for Romanian language using EMO-IIT database with seven emotions (joy, sadness, fury, neutral tone, anxiety, disgust and boredom). Compared to our previous studies we introduced two new emotions: disgust and boredom and a new set of sentences in order to express better the emotional states. The best recognition rate of emotions is around 75% and was obtained for feature vectors which includes MFCC (Mel Frequency Cepstral Coefficients) + PARCOR (Partial Correlations Coefficients) + LAR (Log Area Ratios Coefficients). The accuracy rates are closed to the other studies from the literatures. For example, for the German emotional database EMO-DB which contains all seven emotions, the accuracy recognition rate reported by the researchers was around 85%. The disgust is often recognized as boredom (15%) or neutral tone (10%). The sadness is confused with the neutral tone (12%) and disgust (9%). The main difference between the two databases is that the EMO-IIT contains unprofessional voices with recordings provided by students and EMO-DB contains professional voices, recorded from actors.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Emotion recognition for disgust and boredom states\",\"authors\":\"S. M. Feraru, M. Zbancioc\",\"doi\":\"10.1109/ISSCS.2017.8034913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we made the emotion recognition for Romanian language using EMO-IIT database with seven emotions (joy, sadness, fury, neutral tone, anxiety, disgust and boredom). Compared to our previous studies we introduced two new emotions: disgust and boredom and a new set of sentences in order to express better the emotional states. The best recognition rate of emotions is around 75% and was obtained for feature vectors which includes MFCC (Mel Frequency Cepstral Coefficients) + PARCOR (Partial Correlations Coefficients) + LAR (Log Area Ratios Coefficients). The accuracy rates are closed to the other studies from the literatures. For example, for the German emotional database EMO-DB which contains all seven emotions, the accuracy recognition rate reported by the researchers was around 85%. The disgust is often recognized as boredom (15%) or neutral tone (10%). The sadness is confused with the neutral tone (12%) and disgust (9%). The main difference between the two databases is that the EMO-IIT contains unprofessional voices with recordings provided by students and EMO-DB contains professional voices, recorded from actors.\",\"PeriodicalId\":338255,\"journal\":{\"name\":\"2017 International Symposium on Signals, Circuits and Systems (ISSCS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Symposium on Signals, Circuits and Systems (ISSCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSCS.2017.8034913\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCS.2017.8034913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文利用EMO-IIT数据库对罗马尼亚语进行了情绪识别,包括喜悦、悲伤、愤怒、中性语气、焦虑、厌恶和无聊等七种情绪。与我们之前的研究相比,我们引入了两种新的情绪:厌恶和无聊,为了更好地表达情绪状态,我们还引入了一组新的句子。对于包含MFCC (Mel Frequency倒谱系数)+ parkor(偏相关系数)+ LAR(对数面积比系数)的特征向量,情绪的最佳识别率约为75%。准确率与文献中其他研究接近。例如,对于包含所有七种情绪的德国情绪数据库EMO-DB,研究人员报告的准确率识别率约为85%。厌恶通常被认为是无聊(15%)或中性语气(10%)。悲伤与中性语调(12%)和厌恶(9%)相混淆。两个数据库的主要区别在于,EMO-IIT包含由学生提供的录音的非专业声音,而EMO-DB包含由演员录制的专业声音。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotion recognition for disgust and boredom states
In this paper, we made the emotion recognition for Romanian language using EMO-IIT database with seven emotions (joy, sadness, fury, neutral tone, anxiety, disgust and boredom). Compared to our previous studies we introduced two new emotions: disgust and boredom and a new set of sentences in order to express better the emotional states. The best recognition rate of emotions is around 75% and was obtained for feature vectors which includes MFCC (Mel Frequency Cepstral Coefficients) + PARCOR (Partial Correlations Coefficients) + LAR (Log Area Ratios Coefficients). The accuracy rates are closed to the other studies from the literatures. For example, for the German emotional database EMO-DB which contains all seven emotions, the accuracy recognition rate reported by the researchers was around 85%. The disgust is often recognized as boredom (15%) or neutral tone (10%). The sadness is confused with the neutral tone (12%) and disgust (9%). The main difference between the two databases is that the EMO-IIT contains unprofessional voices with recordings provided by students and EMO-DB contains professional voices, recorded from actors.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信