Sparse Classifier Fusion for Speaker Verification

IEEE J. Sel. Top. Signal Process.

et al. 2017

Self Cite

256

302

Abstract-Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of post-evaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation. Priority future research directions are presented in the scope of the next ASVspoof evaluation planned for 2017.

Section: E Countermeasure Fusionmentioning

confidence: 99%

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Yamagishi

IEEE J. Sel. Top. Signal Process.

et al. 2017

Self Cite

256

302

“…Linear fusion has the benefit that the weights can be interpreted as the relative importance of each classifier and provide insights to the problem at hand. Linear score fusion, in fact, often produces the most competitive results in state-of-the-art speaker verification [50,51]. We do not hand-tune the weights using 340 adhoc grid-search but optimize them using a logistic regression model which provides better generalization.…”

Section: Comparison Of Mfcc and Lfcc Featuresmentioning

confidence: 99%

Source cell-phone recognition from recorded speech using non-speech segments

Hanilçi

Digital Signal Processing

2014

Self Cite

In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yields higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42 % to 98.39 % and equal error rate (EER) reduces from 1.20% to 0.47 % when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.

“…Different classifiers can involve different features, classifiers, or hyper-parameter training sets (Brümmer et al, 2007;Hautamäki et al, 2013b). A simple, yet robust approach to fusion involves the weighted summation of the base classifier scores, where the weights are optimised according to a logistic regression cost function.…”

Section: System Fusionmentioning

confidence: 99%

Spoofing and countermeasures for speaker verification: A survey

Evans

et al. 2015

Speech Communication

Self Cite

552

349

While biometric authentication has advanced significantly in recent years, evidence shows the technology can be susceptible to malicious spoofing attacks. The research community has responded with dedicated countermeasures which aim to detect and deflect such attacks. Even if the literature shows that they can be effective, the problem is far from being solved; biometric systems remain vulnerable to spoofing. Despite a growing momentum to develop spoofing countermeasures for automatic speaker verification, now that the technology has matured sufficiently to support mass deployment in an array of diverse applications, greater effort will be needed in the future to ensure adequate protection against spoofing. This article provides a survey of past work and identifies priority research directions for the future. We summarise previous studies involving impersonation, replay, speech synthesis and voice conversion spoofing attacks and more recent efforts to develop dedicated countermeasures. The survey shows that future research should address the lack of standard datasets and the over-fitting of existing countermeasures to specific, known spoofing attacks.