The SYSU System for the Interspeech 2015 Automatic Speaker Verification Spoofing and Countermeasures Challenge

Weng, Shitao; Chen, Shushan; Yu, Lei; Wu, Xuewei; Cai, Weicheng; Li, Zhi; Li, Ming

doi:10.48550/arxiv.1507.06711

Cited by 1 publication

(6 citation statements)

References 15 publications

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“…Most of the successful spoofing countermeasures use Mel-frequency Cepstral Coefficients (MFCC) with their first and second derivatives as acoustic level features. They were used in [4], [7], [9], [10] and [11]. In [11] authors proposed Linear-frequency Cepstral Coefficients (LFCC) by using linear filterbank instead of mel-filterbank.…”

Section: Front-end Featuresmentioning

confidence: 99%

“…MGD features are known as more stable in speach recognition and were mostly used by participants of the Challenge. They were implemented in spoofing detection systems in [6], [7], [8], [9] and [10]. However experiments from [6] presented in approach to solve the problem of GD were used by [7].…”

Section: Front-end Featuresmentioning

confidence: 99%

“…Phonetic level. Based on the achievements of [19] authors of [9] proposed to use combination of MFCC with the Phonetic Level Phoneme Posterior Probability (PPP) tandem features for spoofing detection task. They used multilayer perceptron based phoneme recognizer with a English acoustic model trained on the TIMIT database for phoneme decoding and obtained 1.72 % EER on their SVM based spoofing detection system.…”

Section: Front-end Featuresmentioning

confidence: 99%

“…These i-vectors were extracted by means of Gaussian factor analyser defined on mean supervectors of the Universal Background Model (UBM) and Total Variability matrix T. UBM was represented by the diagonal covariance Gaussian mixture models of the used features. This approach was also used by [8], [9], while [3] used two simple GMM models for natural and spoofed speech. Systems from [5] used Deep Neural Network (DDN) models.…”

Section: High Level Features Extractionmentioning

confidence: 99%

“…To train SVN we used the efficient LIBLINEAR [13] library with default C-values equal to 1. Authors of [8], [9] and [11] also chose SVM as classifiers in their submitted systems.…”

Section: High Level Features Extractionmentioning

confidence: 99%

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Anti-spoofing Methods for Automatic Speaker Verification System

Lavrentyeva

Novoselov

Simonchik

2017

Communications in Computer and Information Science

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Growing interest in automatic speaker verification (ASV) systems has lead to significant quality improvement of spoofing attacks on them. Many research works confirm that despite the low equal error rate (EER) ASV systems are still vulnerable to spoofing attacks. In this work we overview different acoustic feature spaces and classifiers to determine reliable and robust countermeasures against spoofing attacks. We compared several spoofing detection systems, presented so far, on the development and evaluation datasets of the Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015. Experimental results presented in this paper demonstrate that the use of magnitude and phase information combination provides a substantial input into the efficiency of the spoofing detection systems. Also waveletbased features show impressive results in terms of equal error rate. In our overview we compare spoofing performance for systems based on different classifiers. Comparison results demonstrate that the linear SVM classifier outperforms the conventional GMM approach. However, many researchers inspired by the great success of deep neural networks (DNN) approaches in the automatic speech recognition, applied DNN in the spoofing detection task and obtained quite low EER for known and unknown type of spoofing attacks.

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Section: Front-end Featuresmentioning

confidence: 99%

Section: Front-end Featuresmentioning

confidence: 99%

Section: Front-end Featuresmentioning

confidence: 99%

Section: High Level Features Extractionmentioning

confidence: 99%

“…To train SVN we used the efficient LIBLINEAR [13] library with default C-values equal to 1. Authors of [8], [9] and [11] also chose SVM as classifiers in their submitted systems.…”

Section: High Level Features Extractionmentioning

confidence: 99%

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