Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach

Name: Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach
Author: Süslü, Çağıl, Eren, Eray, Demiroğlu, Cenk

İsim	Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach
Yazar	Süslü, Çağıl, Eren, Eray, Demiroğlu, Cenk
Basım Tarihi:	2022-02
Basım Yeri	- Elsevier
Konu	Automatic speaker verification, Bayesian, Speech, Spoofing countermeasure system, Uncertainty
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0167-6393
Kayıt Numarası	d47415af-edf4-43c7-a3c2-54821cdfdf0c
Lokasyon	Electrical & Electronics Engineering
Tarih	2022-02
Örnek Metin	There has been tremendous progress in automatic speaker verification systems over the last decade. Still, spoofing attacks pose a significant challenge to their deployment. Even though there are various attack techniques such as voice conversion and speech synthesis, replay attacks pose one of the most important types since they can be done without significant expertise in speech technology. Moreover, replay attacks are hard to detect because they are done with simple replay of the original audio. The problem has gained more attention since the introduction of the ASV spoof 2017 challenge, which included a well-designed database with realistic replay attack conditions. Even though many different deep network types and acoustic features were proposed since the challenge, one key issue, which is model uncertainty around the neural networks’ decision is largely ignored. This is a result of using the softmax function with the cross-entropy loss, which is widely used in many domains. Here, we propose using evidential deep learning, which is a recently proposed method that is rapidly gaining popularity, for assessing the model uncertainty around the network's decision. Experimental results show that the investigated network architectures perform better in terms of equal error rate with the new loss function. Moreover, reliability of measured uncertainty is shown by filtering samples out of the test set using the Bayesian uncertainty measure, which resulted with a consistent decrease in EER with decreasing threshold.
DOI	10.1016/j.specom.2021.12.003
Cilt	137

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Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach

Yazar Süslü, Çağıl, Eren, Eray, Demiroğlu, Cenk

Basım Tarihi 2022-02

Basım Yeri - Elsevier

Konu Automatic speaker verification, Bayesian, Speech, Spoofing countermeasure system, Uncertainty

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0167-6393

Kayıt Numarası d47415af-edf4-43c7-a3c2-54821cdfdf0c

Lokasyon Electrical & Electronics Engineering

Tarih 2022-02

Örnek Metin There has been tremendous progress in automatic speaker verification systems over the last decade. Still, spoofing attacks pose a significant challenge to their deployment. Even though there are various attack techniques such as voice conversion and speech synthesis, replay attacks pose one of the most important types since they can be done without significant expertise in speech technology. Moreover, replay attacks are hard to detect because they are done with simple replay of the original audio. The problem has gained more attention since the introduction of the ASV spoof 2017 challenge, which included a well-designed database with realistic replay attack conditions. Even though many different deep network types and acoustic features were proposed since the challenge, one key issue, which is model uncertainty around the neural networks’ decision is largely ignored. This is a result of using the softmax function with the cross-entropy loss, which is widely used in many domains. Here, we propose using evidential deep learning, which is a recently proposed method that is rapidly gaining popularity, for assessing the model uncertainty around the network's decision. Experimental results show that the investigated network architectures perform better in terms of equal error rate with the new loss function. Moreover, reliability of measured uncertainty is shown by filtering samples out of the test set using the Bayesian uncertainty measure, which resulted with a consistent decrease in EER with decreasing threshold.

DOI 10.1016/j.specom.2021.12.003

Cilt 137