Anti-Spoofing for Text-Independent Speaker Verification: An Initial Database, Comparison of Countermeasures, and Human Performance

Wu, Zhizheng; León, Phillip L. De; Demiroğlu, Cenk; Khodabakhsh, Ali; King, Simon; Ling, Zhen-Hua; Saito, Daisuke; Stewart, Bryan; Toda, Tomoki; Wester, Mirjam; Yamagishi, Junichi

doi:10.1109/taslp.2016.2526653

Cited by 94 publications

(51 citation statements)

References 49 publications

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“…The baseline system is based on the voice conversion toolkit within the open-source Festvox system 2 , as in our previous work [40], we found the toolkit can achieve similar performance to other state-of-the-art voice conversion or speech synthesis adaptation techniques. The toolkit is based on the joint density Gaussian mixture model with maximum likelihood parameter trajectory generation considering global variance as proposed in [27].…”

Section: Baseline Systemmentioning

confidence: 58%

The Voice Conversion Challenge 2016

et al. 2016

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This paper describes the Voice Conversion Challenge 2016 devised by the authors to better understand different voice conversion (VC) techniques by comparing their performance on a common dataset. The task of the challenge was speaker conversion, i.e., to transform the voice identity of a source speaker into that of a target speaker while preserving the linguistic content. Using a common dataset consisting of 162 utterances for training and 54 utterances for evaluation from each of 5 source and 5 target speakers, 17 groups working in VC around the world developed their own VC systems for every combination of the source and target speakers, i.e., 25 systems in total, and generated voice samples converted by the developed systems. These samples were evaluated in terms of target speaker similarity and naturalness by 200 listeners in a controlled environment. This paper summarizes the design of the challenge, its result, and a future plan to share views about unsolved problems and challenges faced by the current VC techniques.

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Section: Baseline Systemmentioning

confidence: 58%

The Voice Conversion Challenge 2016

et al. 2016

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“…software available to the public, including fraudsters. More details including protocols used to generate spoofed speech can be found in [6], [7]. Figure 2 provides an intuitive visualisation of the acoustic similarities between each spoofing-attack algorithm and genuine speech.…”

Section: S10mentioning

confidence: 99%

“…The S1 and S2 spoofing-attack algorithms are two of the most easily implemented voiceconversion algorithms. The S3, S4, and S5 spoofing-attack algorithms were all implemented using either the HTS 6 or Festvox 7 , both of which are publicly available and open source. The remaining unknown attacks (S6-S10) are generally more sophisticated.…”

Section: Protocolsmentioning

confidence: 99%

“…They include the series of LivDet evaluations for fingerprint recognition [8] and a similar initiative for face recognition [9]. As of 2014, [6], [7], the first ASVspoof challenge was co-organized in 2015. The initiative lead to the submission of 16 countermeasure systems, all benchmarked and ranked using a common database, protocol, and metric.…”

mentioning

confidence: 99%

“…Stemming from the special session at INTERSPEECH, the creation of a large dataset of genuine and spoofed speech [6], [7] started later in 2013. Following refinements and improvements to the dataset and protocols, the first ASVspoof challenge [11] 1 was organised as a special session at IN-TERSPEECH 2015 held in Dresden, Germany.…”

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confidence: 99%

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ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Yamagishi

Kinnunen

et al. 2017

IEEE J. Sel. Top. Signal Process.

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Abstract-Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes the ASV Spoofing and Countermeasures (ASVspoof) initiative, which aims to fill this void. Through the provision of a common dataset, protocols, and metrics, ASVspoof promotes a sound research methodology and fosters technological progress. This paper also describes the ASVspoof 2015 dataset, evaluation, and results with detailed analyses. A review of post-evaluation studies conducted using the same dataset illustrates the rapid progress stemming from ASVspoof and outlines the need for further investigation. Priority future research directions are presented in the scope of the next ASVspoof evaluation planned for 2017.

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Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model

Liu

Odobez

2019

Lecture Notes in Computer Science

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The step preceding the speaker identification process consists in the determination of the authenticity of a speech sample. The focus of this thesis is on the performance of humans in detecting altered samples from replay, speech synthesis (TTS) and voice conversion (VC) systems. A listening test was constructed on the online survey platform LimeSurvey. The participants were asked to assess a series of recordings by first giving a binary evaluation ("authentic" or "altered") and then by specifying their level of confidence on a 5-point scale. Moreover, the logic behind the aural approach was studied by inspecting the criteria used by the respondents in the assessment of the recordings. The same samples were also evaluated with an automatic LFCC-GMM-based system, trained on two different datasets, in order to make a comparison. The results show that the human's performance (EER=0.10) surpasses the machine's (EER=0.35 and EER=0.46) in the detection of the altered samples. However, sophisticated voice disguise systems have now reached levels of quality that can also easily fool most humans, which makes them a real threat to security and the identification process.

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Anti-Spoofing for Text-Independent Speaker Verification: An Initial Database, Comparison of Countermeasures, and Human Performance

Cited by 94 publications

References 49 publications

The Voice Conversion Challenge 2016

The Voice Conversion Challenge 2016

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model

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