{"title":"印度语言的多任务合成语音检测","authors":"A. R. Ambili, Rajesh Cherian Roy","doi":"10.1109/ICITIIT54346.2022.9744221","DOIUrl":null,"url":null,"abstract":"Anti-spoofing research plays an important role in audio forensics. It has found a lot of traction in several languages around the world. With that in mind, the purpose of this work is to assess the impact of several synthetic spoofing detection approaches on a multilingual, low-constrained Indian language set. This paper aims at a multitasking spoofing detection by identifying real/spoof utterance identification as well as the regional language spoofing attack vector. To accomplish this, the features and the classifiers that are best candidate for the synthetic spoofing detection and language identification are appropriately chosen. Our methodology compares the performances of three main different classifiers GMM, SVM, DNN on the vector formulated from the accumulation of MFCC features. Hindi, Malayalam, Tamil, Telugu are the four languages which are taken into account for the comparison. Out of these classifiers, SVM and DNN are found to give the best results with EER rates of 1.98 % and 1.19 % respectively.","PeriodicalId":184353,"journal":{"name":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multi Tasking Synthetic Speech Detection on Indian Languages\",\"authors\":\"A. R. Ambili, Rajesh Cherian Roy\",\"doi\":\"10.1109/ICITIIT54346.2022.9744221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anti-spoofing research plays an important role in audio forensics. It has found a lot of traction in several languages around the world. With that in mind, the purpose of this work is to assess the impact of several synthetic spoofing detection approaches on a multilingual, low-constrained Indian language set. This paper aims at a multitasking spoofing detection by identifying real/spoof utterance identification as well as the regional language spoofing attack vector. To accomplish this, the features and the classifiers that are best candidate for the synthetic spoofing detection and language identification are appropriately chosen. Our methodology compares the performances of three main different classifiers GMM, SVM, DNN on the vector formulated from the accumulation of MFCC features. Hindi, Malayalam, Tamil, Telugu are the four languages which are taken into account for the comparison. Out of these classifiers, SVM and DNN are found to give the best results with EER rates of 1.98 % and 1.19 % respectively.\",\"PeriodicalId\":184353,\"journal\":{\"name\":\"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITIIT54346.2022.9744221\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Innovative Trends in Information Technology (ICITIIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITIIT54346.2022.9744221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi Tasking Synthetic Speech Detection on Indian Languages
Anti-spoofing research plays an important role in audio forensics. It has found a lot of traction in several languages around the world. With that in mind, the purpose of this work is to assess the impact of several synthetic spoofing detection approaches on a multilingual, low-constrained Indian language set. This paper aims at a multitasking spoofing detection by identifying real/spoof utterance identification as well as the regional language spoofing attack vector. To accomplish this, the features and the classifiers that are best candidate for the synthetic spoofing detection and language identification are appropriately chosen. Our methodology compares the performances of three main different classifiers GMM, SVM, DNN on the vector formulated from the accumulation of MFCC features. Hindi, Malayalam, Tamil, Telugu are the four languages which are taken into account for the comparison. Out of these classifiers, SVM and DNN are found to give the best results with EER rates of 1.98 % and 1.19 % respectively.