SFF Anti-Spoofer: IIIT-H Submission for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017

Alluri, K. N. R. K. Raju; Achanta, Sivanand; Kadiri, Sudarsana Reddy; Gangashetty, Suryakanth V.; Vuppala, Anil Kumar

doi:10.21437/interspeech.2017-676

Cited by 44 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effect of mean and variance normalization of CQCC feature set with Support Vector Machines (SVM) classifier was studied in [81,118]. One of the approaches used Single Frequency Filtering (SFF) and found the importance of high-resolution temporal features [119].…”

Section: ) Acoustic Featuresmentioning

confidence: 99%

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

Kamble

Sailor

Patil

et al. 2020

SIP

View full text Add to dashboard Cite

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.

show abstract

Section: ) Acoustic Featuresmentioning

confidence: 99%

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

Kamble

Sailor

Patil

et al. 2020

SIP

View full text Add to dashboard Cite

show abstract

“…• Zero time windowing-based features: zero time windowing cepstral coefficients [46,47]. • Single frequency filter-based features: single frequency filter cepstral coefficients [3,47].…”

Section: • Prediction Cepstral Coefficients-based Featuresmentioning

confidence: 99%

“…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].…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…Constant Q cepstral coefficients (CQCC), which is proposed by Todisco M et al [7], was adopted in the baseline system for this challenge. After that, various features were used in recent literature to improve the performance of replay detection, such as the inverted Mel-frequency cepstral coefficients (IMFCC) [8], single frequency filtering coefficients (SFFCC) [9], high-frequency cepstral coefficients (HFCC) [10], and linear frequency cepstral coefficients (LFCC) [11]. All these works used CQCC features as baseline features and a Gaussian Mixture Model (GMM) classifier for the final classification.…”

Section: Introductionmentioning

confidence: 99%

A Robust Method for Speech Replay Attack Detection

2020

KSII TIIS

View full text Add to dashboard Cite

Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems. Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability. In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel. Then a novel feature is proposed to detect the replay attacks. To further boost the detection performance, four post-processing methods using normalization techniques are investigated. We evaluate our proposed method on the ASVspoof 2017 dataset. The experimental results show that our approach outperforms the competing methods in terms of detection accuracy. More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.

show abstract

SFF Anti-Spoofer: IIIT-H Submission for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017

Cited by 44 publications

References 22 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

A Robust Method for Speech Replay Attack Detection

Contact Info

Product

Resources

About