A Replay Speech Detection Algorithm Based on Sub-band Analysis

Lin, Lang; Wang, Rangding; Yan, Diqun

doi:10.1007/978-3-030-00828-4_34

Cited by 6 publications

(10 citation statements)

References 13 publications

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“…The results here agree with the finding in the study [41], which found that 0-0.5 kHz and 7-8 kHz sub-bands were more discriminative than other frequency bands. Another study [42], which used the RP for analysis, reported that 0-1 and 4-5 kHz were more informative and discriminative.…”

Section: E) Frequency Importance Analysissupporting

confidence: 93%

A multi-branch ResNet with discriminative features for detection of replay speech signals

Cheng

Zheng

2020

SIP

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Nowadays, the security of ASV systems is increasingly gaining attention. As one of the common spoofing methods, replay attacks are easy to implement but difficult to detect. Many researchers focus on designing various features to detect the distortion of replay attack attempts. Constant-Q cepstral coefficients (CQCC), based on the magnitude of the constant-Q transform (CQT), is one of the striking features in the field of replay detection. However, it ignores phase information, which may also be distorted in the replay processes. In this work, we propose a CQT-based modified group delay feature (CQTMGD) which can capture the phase information of CQT. Furthermore, a multi-branch residual convolution network, ResNeWt, is proposed to distinguish replay attacks from bonafide attempts. We evaluated our proposal in the ASVspoof 2019 physical access dataset. Results show that CQTMGD outperformed the traditional MGD feature, and the fusion with other magnitude-based and phase-based features achieved a further improvement. Our best fusion system achieved 0.0096 min-tDCF and 0.39% EER on the evaluation set and it outperformed all the other state-of-the-art methods in the ASVspoof 2019 physical access challenge.

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Section: E) Frequency Importance Analysissupporting

confidence: 93%

A multi-branch ResNet with discriminative features for detection of replay speech signals

Cheng

Zheng

2020

SIP

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“…In the context of spoofing detection, the most relevant studies include [19,20,21,22,23,24]. The authors of [19] investigated different subbands to find the most informative bands useful for spoofing detection tasks.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

et al. 2019

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Spectrograms -time-frequency representations of audio signals -have found widespread use in neural network-based spoofing detection. While deep models are trained on the fullband spectrum of the signal, we argue that not all frequency bands are useful for these tasks. In this paper, we systematically investigate the impact of different subbands and their importance on replay spoofing detection on two benchmark datasets: ASVspoof 2017 v2.0 and ASVspoof 2019 PA. We propose a joint subband modelling framework that employs n different sub-networks to learn subband specific features. These are later combined and passed to a classifier and the whole network weights are updated during training. Our findings on the ASVspoof 2017 dataset suggest that the most discriminative information appears to be in the first and the last 1 kHz frequency bands, and the joint model trained on these two subbands shows the best performance outperforming the baselines by a large margin. However, these findings do not generalise on the ASVspoof 2019 PA dataset. This suggests that the datasets available for training these models do not reflect real world replay conditions suggesting a need for careful design of datasets for training replay spoofing countermeasures.

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“…In our previous research on ASVspoof 2017 dataset, we performed a detailed analysis of the differences between genuine speech and the replay speech on the frequency sub-bands. Our research showed that the discriminative information of genuine speech and replay speech is mainly distributed in two sub-bands, i.e., 0-1 kHz and 7-8 kHz [12] [13]. one plausible explanation for this observation can be as follows.…”

Section: Introductionmentioning

confidence: 66%

A Robust Method for Speech Replay Attack Detection

2020

KSII TIIS

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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.

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A Replay Speech Detection Algorithm Based on Sub-band Analysis

Cited by 6 publications

References 13 publications

A multi-branch ResNet with discriminative features for detection of replay speech signals

A multi-branch ResNet with discriminative features for detection of replay speech signals

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

A Robust Method for Speech Replay Attack Detection

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