2014
DOI: 10.1007/s10772-014-9235-7
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New scheme based on GMM-PCA-SVM modelling for automatic speaker recognition

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Cited by 13 publications
(4 citation statements)
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“…In the previous work, the traditional features such as MFCC [27], Gaussian statistical characteristics [15] were often applied to speaker identification, which had good performance. In this paper, we extract 48-dimension MFCC from the input speech and calculate the Gaussian statistics as the input feature to train the SVM.…”
Section: Recombined Gaussian Supervectormentioning
confidence: 99%
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“…In the previous work, the traditional features such as MFCC [27], Gaussian statistical characteristics [15] were often applied to speaker identification, which had good performance. In this paper, we extract 48-dimension MFCC from the input speech and calculate the Gaussian statistics as the input feature to train the SVM.…”
Section: Recombined Gaussian Supervectormentioning
confidence: 99%
“…MFCC represents the transient power range of human speech [15]. Mel frequency reflects the conversion relationship between actual frequency and perceptual frequency.…”
Section: Mfccmentioning
confidence: 99%
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“…For digital speech signals, there are a lot of research results in speech enhancement (Shima, Ahmad, & Babak, 2015;Ji & Danny, 2014;Mohamed & Pascal, 2014;Belinda & Kuldip, 2014;Seon & Hong, 2014) and speaker recognition (Shiha, Linb, Wanga, & Lina, 2011;Srikanth, 2014, pp. 137-145;Kawthar & Abderrahmane, 2014;Khan, Baig, & Youssef, 2010). However the speech content authentication schemes are rare.…”
Section: Introductionmentioning
confidence: 99%