A. Smailagic, D. Siewiorek, A. Rudnicky, Sandeep Nallan Chakravarthula, Anshuman Kar, Nivedita Jagdale, Saksham Gautam, Rohit Vijayaraghavan, S. Jagtap
{"title":"情绪识别调节智能系统的行为","authors":"A. Smailagic, D. Siewiorek, A. Rudnicky, Sandeep Nallan Chakravarthula, Anshuman Kar, Nivedita Jagdale, Saksham Gautam, Rohit Vijayaraghavan, S. Jagtap","doi":"10.1109/ISM.2013.72","DOIUrl":null,"url":null,"abstract":"The paper presents an audio-based emotion recognition system that is able to classify emotions as anger, fear, happy, neutral, sadness or disgust in real time. We use the virtual coach as an application example of how emotion recognition can be used to modulate intelligent systems' behavior. A novel minimum-error feature removal mechanism to reduce bandwidth and increase accuracy of our emotion recognition system has been introduced. A two-stage hierarchical classification approach along with a One-Against-All (OAA) framework are used. We obtained an average accuracy of 82.07% using the OAA approach, and 87.70% with a two-stage hierarchical approach, by pruning the feature set and using Support Vector Machines (SVMs) for classification.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"8 1","pages":"378-383"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Emotion Recognition Modulating the Behavior of Intelligent Systems\",\"authors\":\"A. Smailagic, D. Siewiorek, A. Rudnicky, Sandeep Nallan Chakravarthula, Anshuman Kar, Nivedita Jagdale, Saksham Gautam, Rohit Vijayaraghavan, S. Jagtap\",\"doi\":\"10.1109/ISM.2013.72\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents an audio-based emotion recognition system that is able to classify emotions as anger, fear, happy, neutral, sadness or disgust in real time. We use the virtual coach as an application example of how emotion recognition can be used to modulate intelligent systems' behavior. A novel minimum-error feature removal mechanism to reduce bandwidth and increase accuracy of our emotion recognition system has been introduced. A two-stage hierarchical classification approach along with a One-Against-All (OAA) framework are used. We obtained an average accuracy of 82.07% using the OAA approach, and 87.70% with a two-stage hierarchical approach, by pruning the feature set and using Support Vector Machines (SVMs) for classification.\",\"PeriodicalId\":6311,\"journal\":{\"name\":\"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)\",\"volume\":\"8 1\",\"pages\":\"378-383\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISM.2013.72\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2013.72","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Emotion Recognition Modulating the Behavior of Intelligent Systems
The paper presents an audio-based emotion recognition system that is able to classify emotions as anger, fear, happy, neutral, sadness or disgust in real time. We use the virtual coach as an application example of how emotion recognition can be used to modulate intelligent systems' behavior. A novel minimum-error feature removal mechanism to reduce bandwidth and increase accuracy of our emotion recognition system has been introduced. A two-stage hierarchical classification approach along with a One-Against-All (OAA) framework are used. We obtained an average accuracy of 82.07% using the OAA approach, and 87.70% with a two-stage hierarchical approach, by pruning the feature set and using Support Vector Machines (SVMs) for classification.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
