{"title":"基于gmm和svm的语音频移检测","authors":"Hua Xing, P. Loizou","doi":"10.1109/SiPS.2012.23","DOIUrl":null,"url":null,"abstract":"In certain situations, speech might be shifted in the frequency domain amid the presence of noise. To be able to compensate for the spectral shift, it is important to know the amount of frequency shift present. A method based on Mel-frequency-cepstral-coefficient (MFCC) and Gaussian Mixture model (GMM) super vector is proposed for detecting frequency shifts in speech. MFCC or LFCC is extracted to characterize the energy variation of the signal. A GMM is trained for each shifted utterance, and the corresponding GMM super vector is used as the input feature for SVM. Results show that the proposed solution could yield good performance.","PeriodicalId":286060,"journal":{"name":"2012 IEEE Workshop on Signal Processing Systems","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Frequency Shift Detection of Speech with GMMs AND SVMs\",\"authors\":\"Hua Xing, P. Loizou\",\"doi\":\"10.1109/SiPS.2012.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In certain situations, speech might be shifted in the frequency domain amid the presence of noise. To be able to compensate for the spectral shift, it is important to know the amount of frequency shift present. A method based on Mel-frequency-cepstral-coefficient (MFCC) and Gaussian Mixture model (GMM) super vector is proposed for detecting frequency shifts in speech. MFCC or LFCC is extracted to characterize the energy variation of the signal. A GMM is trained for each shifted utterance, and the corresponding GMM super vector is used as the input feature for SVM. Results show that the proposed solution could yield good performance.\",\"PeriodicalId\":286060,\"journal\":{\"name\":\"2012 IEEE Workshop on Signal Processing Systems\",\"volume\":\"2012 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Workshop on Signal Processing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SiPS.2012.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Workshop on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SiPS.2012.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Frequency Shift Detection of Speech with GMMs AND SVMs
In certain situations, speech might be shifted in the frequency domain amid the presence of noise. To be able to compensate for the spectral shift, it is important to know the amount of frequency shift present. A method based on Mel-frequency-cepstral-coefficient (MFCC) and Gaussian Mixture model (GMM) super vector is proposed for detecting frequency shifts in speech. MFCC or LFCC is extracted to characterize the energy variation of the signal. A GMM is trained for each shifted utterance, and the corresponding GMM super vector is used as the input feature for SVM. Results show that the proposed solution could yield good performance.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
