SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

Campbell, William M.; Sturim, Douglas E.; Reynolds, Douglas A.; Solomonoff, A.

doi:10.1109/icassp.2006.1659966

Cited by 426 publications

(275 citation statements)

References 7 publications

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“…This approach is based on the use of a kernel calculated using a distance between GMM in the model space defined in [10]. This approach can be compared with two others methods presented in [6] and [11]. The difference between our approach and the method in [6] lies in the fact that the method given in [6] doesn't use any normalization of the GMMs models.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Gmms for Speaker Verification

Dehak

Chollet

2006

2006 IEEE Odyssey - The Speaker and Language Recognition Workshop

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This article presents a new approach using the discrimination power of Support Vectors Machines (SVM) in combination with Gaussian Mixture Models (GMM) for Automatic Speaker Verification (ASV). In this combination SVMs are applied in the GMM model space. Each point of this space represents a GMM speaker model. The kernel which is used for the SVM allows the computation of a similarity between GMM models. It was calculated using the Kullback-Leibler (KL) divergence. The results of this new approach show a clear improvement compared to a simple GMM system on the NIST2005 Speaker Recognition Evaluation primary task.

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Section: Introductionmentioning

confidence: 99%

Support Vector Gmms for Speaker Verification

Dehak

Chollet

2006

2006 IEEE Odyssey - The Speaker and Language Recognition Workshop

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“…It is referred to as a GMM supervector linear kernel system (GSL). The fourth system is almost identical to the third but is enhanced with nuisance attribute projection [29] to attenuate intersession (interchannel) variability, with NAP matrices of rank 40. The fifth approach is a GSL system with FA supervectors (GSL-FA) [26].…”

Section: Asv Systemsmentioning

confidence: 99%

Spoofing countermeasures to protect automatic speaker verification from voice conversion

Alegre

Amehraye

Evans

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure exploits the common shift applied to the spectral slope of consecutive speech frames involved in the mapping of a spoofer's voice signal towards a statistical model of a given target. While the countermeasure exploits prior knowledge of the attack in an admittedly unrealistic sense, it is shown to detect almost all spoofed signals which otherwise provoke significant increases in false acceptance. The work also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge.

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“…Each speaker utterance is modeled using a MAP-adapted GMM supervector [23] and compared to other utterances via a Euclidean distance metric as described in [4]. Lastly, the global scaling parameter σ from Eqn.…”

Section: Setupmentioning

confidence: 99%

Large-scale community detection on speaker content graphs

Shum¹,

Campbell

Reynolds

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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We consider the use of community detection algorithms to perform speaker clustering on content graphs built from large audio corpora. We survey the application of agglomerative hierarchical clustering, modularity optimization methods, and spectral clustering as well as two random walk algorithms: Markov clustering and Infomap. Our results on graphs built from the NIST 2005+2006 and 2008+2010 Speaker Recognition Evaluations (SREs) provide insight into both the structure of the speakers present in the data and the intricacies of the clustering methods. In particular, we introduce an additional parameter to Infomap that improves its clustering performance on all graphs. Lastly, we also develop an automatic technique to purify the neighbors of each node by pruning away unnecessary edges.

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SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

Cited by 426 publications

References 7 publications

Support Vector Gmms for Speaker Verification

Support Vector Gmms for Speaker Verification

Spoofing countermeasures to protect automatic speaker verification from voice conversion

Large-scale community detection on speaker content graphs

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