Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

In recent years, automatic speaker verification (ASV) is used extensively for voice biometrics. This leads to an increased interest to secure these voice biometric systems for real-world applications. The ASV systems are vulnerable to various kinds of spoofing attacks, namely, synthetic speech (SS), voice conversion (VC), replay, twins, and impersonation. This paper provides the literature review of ASV spoof detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Furthermore, the paper also summaries previous studies of spoofing attacks with emphasis on SS, VC, and replay along with recent efforts to develop countermeasures for spoof speech detection (SSD) task. The limitations and challenges of SSD task are also presented. While several countermeasures were reported in the literature, they are mostly validated on a particular database, furthermore, their performance is far from perfect. The security of voice biometrics systems against spoofing attacks remains a challenging topic. This paper is based on a tutorial presented at APSIPA Annual Summit and Conference 2017 to serve as a quick start for those interested in the topic.

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“…The ConvRBM subband filters in temporal-domain (a) without, and (b) with pre-emphasis, respectively. After [139].…”

Section: ) Representation Of Learning Approachesmentioning

confidence: 99%

Advances in anti-spoofing: from the perspective of ASVspoof challenges

Kamble

Sailor

Patil

et al. 2020

SIP

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“…Since the ASVspoof 2017 challenge [1,2], more and more researchers begin to focus on playback speech detection [3][4][5][6][7][8][9][10]. Similar to many speech signal processing systems, most of all playback speech detection systems usually consist of front-end feature and back-end classifier [11][12][13][14][15][16][17][18]. For the end-to-end systems such as *Correspondence: xlt@dhu.edu.cn † Jichen Yang and Longting Xu contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Discriminative features based on modified log magnitude spectrum for playback speech detection

Yang

Ren³

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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In order to improve the performance of hand-crafted features to detect playback speech, two discriminative features, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients, are proposed for playback speech detection in this work. They rely on our findings that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can enhance the discriminative power between genuine speech and playback speech. Then constant-Q variance-based octave coefficients (constant-Q mean-based octave coefficients) can be obtained by combining variance-based modified log magnitude spectrum (mean-based modified log magnitude spectrum), octave segmentation, and discrete cosine transform. Finally, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients are evaluated on ASVspoof 2017 corpus version 2.0 and ASVspoof 2019 physical access, respectively. Experimental results show that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can produce discriminative features toward playback speech. Further results on the two databases show that constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients can perform better than some common features, such as mel frequency cepstral coefficients and constant-Q cepstral coefficients.

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“…In some other efforts, DNN framework is used [24,25,26,27,28]. For replay detection, algorithms were proposed using Electronic Network Frequency (ENF), MFCC and fundamental frequency, linear predictive residual signal, time envelope, stratified scattering decomposition coefficient and Inverse MFCC (IMFCC) respectively [29,30,31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Wang

Zhu

2020

Preprint

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Nowadays, speaker disguise is a common operation that presents a great challenge to social security. Therefore, it is important to recognize the authenticity of speech. Most of the current researches focus on speech spoofing, which simulates a target speaker to break through the state-of-art ASV systems by increasing false acceptance rate. Meanwhile, there is another type of disguise, i.e. de-identification, which transforms a speech signal without a target to increase the false rejection rate in order not to be recognized. It has received far less attention. Therefore, in this paper, we investigate the de-identification model and propose a method to detect de-identification speeches from genuine speeches by using a very deep dense convolutional network with 135 layers. The experimental results show that the average accuracy of the proposed method outperforms the reported state-of-the-art methods.

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Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

Cited by 33 publications

References 33 publications

Advances in anti-spoofing: from the perspective of ASVspoof challenges

Advances in anti-spoofing: from the perspective of ASVspoof challenges

Discriminative features based on modified log magnitude spectrum for playback speech detection

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

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