{"title":"低调浅学习语音欺骗检测系统","authors":"Dalal Ali, S. Al-Shareeda, Najla Abdulrahman","doi":"10.1109/MENACOMM57252.2022.9998199","DOIUrl":null,"url":null,"abstract":"This paper creates a Gaussian shallow learning Mixture Model (GMM) voice-replay detector using the MATLAB low-key machine learning and statistics libraries. Our model extracts the Mel frequency cepstrum coefficients (MFCC) and constant Q cepstrum coefficients (CQCC) from the input voice signal in the front-end feature extraction stage. The collected characteristics are fed to the constructed GMM classifier to categorize the input voice as either authentic from a live source or replayed from a prerecorded source. The GMM is trained using large datasets of voice feature samples representing both classes. The classifier’s performance is measured using the Equal Error Rate (%EER) metric. To optimize performance, we subject the trained GMM to substantial development and assessment datasets in diverse scenarios and settings of reduction, normalization, and filtration. The best %EER results for the GMM classifier are 11.2237% for the development set and 22.5429% for the evaluation set.","PeriodicalId":332834,"journal":{"name":"2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Low-Key Shallow Learning Voice Spoofing Detection System\",\"authors\":\"Dalal Ali, S. Al-Shareeda, Najla Abdulrahman\",\"doi\":\"10.1109/MENACOMM57252.2022.9998199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper creates a Gaussian shallow learning Mixture Model (GMM) voice-replay detector using the MATLAB low-key machine learning and statistics libraries. Our model extracts the Mel frequency cepstrum coefficients (MFCC) and constant Q cepstrum coefficients (CQCC) from the input voice signal in the front-end feature extraction stage. The collected characteristics are fed to the constructed GMM classifier to categorize the input voice as either authentic from a live source or replayed from a prerecorded source. The GMM is trained using large datasets of voice feature samples representing both classes. The classifier’s performance is measured using the Equal Error Rate (%EER) metric. To optimize performance, we subject the trained GMM to substantial development and assessment datasets in diverse scenarios and settings of reduction, normalization, and filtration. The best %EER results for the GMM classifier are 11.2237% for the development set and 22.5429% for the evaluation set.\",\"PeriodicalId\":332834,\"journal\":{\"name\":\"2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MENACOMM57252.2022.9998199\",\"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 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MENACOMM57252.2022.9998199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low-Key Shallow Learning Voice Spoofing Detection System
This paper creates a Gaussian shallow learning Mixture Model (GMM) voice-replay detector using the MATLAB low-key machine learning and statistics libraries. Our model extracts the Mel frequency cepstrum coefficients (MFCC) and constant Q cepstrum coefficients (CQCC) from the input voice signal in the front-end feature extraction stage. The collected characteristics are fed to the constructed GMM classifier to categorize the input voice as either authentic from a live source or replayed from a prerecorded source. The GMM is trained using large datasets of voice feature samples representing both classes. The classifier’s performance is measured using the Equal Error Rate (%EER) metric. To optimize performance, we subject the trained GMM to substantial development and assessment datasets in diverse scenarios and settings of reduction, normalization, and filtration. The best %EER results for the GMM classifier are 11.2237% for the development set and 22.5429% for the evaluation set.