Robust regression fusion of GMM-UBM and GMM-SVM normalized scores using G729 bit-stream for speaker recognition over IP

Yessad, Dalila; Amrouche, Abderrahmane

doi:10.1007/s10772-013-9204-6

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Figure 3 Timeline View Of the Most Significant Developments In Features Extraction Techniques1

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Cited by 4 publications

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“…The reported results revealed that the MFCC features still outperform the other features, even in stressed conditions. In a further study [42], the MFCC and the LPCC futures were also assessed in speech resynthesized conditions. The assessment was done using a GMM-UBM based speaker verification system of 128 Gaussians.…”

Section: Figure 3 Timeline View Of the Most Significant Developments In Features Extraction Techniquesmentioning

confidence: 99%

Towards an objective comparison of feature extraction techniques for automatic speaker recognition systems

2021

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A common limitation of the previous comparative studies on speaker-features extraction techniques lies in the fact that the comparison is done independently of the used speaker modeling technique and its parameters. The aim of the present paper is twofold. Firstly, it aims to review the most significant advancements in feature extraction techniques used for automatic speaker recognition. Secondly, it seeks to evaluate and compare the currently dominant ones using an objective comparison methodology that overcomes the various limitations and drawbacks of the previous comparative studies. The results of the carried out experiments underlines the importance of the proposed comparison methodology.

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Section: Figure 3 Timeline View Of the Most Significant Developments In Features Extraction Techniquesmentioning

confidence: 99%