{"title":"基于多核学习的跨语言语音情感识别","authors":"Cheng Zha","doi":"10.1109/icsgea.2019.00055","DOIUrl":null,"url":null,"abstract":"Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-Language Speech Emotion Recognition via Multiple Kernel Learning\",\"authors\":\"Cheng Zha\",\"doi\":\"10.1109/icsgea.2019.00055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.\",\"PeriodicalId\":201721,\"journal\":{\"name\":\"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icsgea.2019.00055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icsgea.2019.00055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cross-Language Speech Emotion Recognition via Multiple Kernel Learning
Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.