{"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}
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.