{"title":"基于基本情绪状态信息的说话人验证","authors":"P. Staroniewicz","doi":"10.1109/IWBF.2016.7449689","DOIUrl":null,"url":null,"abstract":"The paper presents speaker verification results for six basic emotional states. The database of emotional speech (six acted states: anger, sadness, happiness, fear, disgust, surprise) plus the neutral state were examined with a typical speaker verification system based on MFCC features and GMM classifiers. The obtained results were confronted with the subjective and objective emotion recognition scores. The significant influence of emotion on speaker recognition scores is substantially bigger for emotions with a strong activation factor.","PeriodicalId":282164,"journal":{"name":"2016 4th International Conference on Biometrics and Forensics (IWBF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Considering basic emotional state information in speaker verification\",\"authors\":\"P. Staroniewicz\",\"doi\":\"10.1109/IWBF.2016.7449689\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents speaker verification results for six basic emotional states. The database of emotional speech (six acted states: anger, sadness, happiness, fear, disgust, surprise) plus the neutral state were examined with a typical speaker verification system based on MFCC features and GMM classifiers. The obtained results were confronted with the subjective and objective emotion recognition scores. The significant influence of emotion on speaker recognition scores is substantially bigger for emotions with a strong activation factor.\",\"PeriodicalId\":282164,\"journal\":{\"name\":\"2016 4th International Conference on Biometrics and Forensics (IWBF)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 4th International Conference on Biometrics and Forensics (IWBF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWBF.2016.7449689\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 4th International Conference on Biometrics and Forensics (IWBF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWBF.2016.7449689","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Considering basic emotional state information in speaker verification
The paper presents speaker verification results for six basic emotional states. The database of emotional speech (six acted states: anger, sadness, happiness, fear, disgust, surprise) plus the neutral state were examined with a typical speaker verification system based on MFCC features and GMM classifiers. The obtained results were confronted with the subjective and objective emotion recognition scores. The significant influence of emotion on speaker recognition scores is substantially bigger for emotions with a strong activation factor.