ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

Wang, Xin; Yamagishi, Junichi; Todisco, Massimiliano; Delgado, Héctor; Nautsch, Andreas; Evans, Nicholas; Sahidullah, Md; Vestman, Ville; Kinnunen, Tomi; Lee, Kong Aik; Juvela, Lauri; Alku, Paavo; Peng, Yuhuai; Hwang, Hsin-Te; Tsao, Yu; Wang, Hsin‐Min; Maguer, Sébastien Le; Becker, Markus C.; Henderson, Fergus; Clark, Rob; Zhang, Yu; Wang, Quan; Jia, Yuheng; Onuma, Kai; Mushika, Koji; Kaneda, Takashi; Jiang, Yuan; Liu, Lijuan; Wu, Yi-Chiao; Huang, Wen-Chin; Toda, Tomoki; Tanaka, Kou; Kameoka, Hirokazu; Steiner, Ingmar; Matrouf, Driss; Bonastre, Jean-François; Govender, Avashna; Ronanki, Srikanth; Zhang, Jingxuan; Ling, Zhen-Hua

doi:10.1016/j.csl.2020.101114

Cited by 235 publications

(158 citation statements)

References 11 publications

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“…The proposed features have been compared with the recently proposed baseline method [9] exploiting bicoherence analysis on the ASVspoof 2019 dataset [17]. The results show that the proposed method outperforms the bicoherence-based one in the binary, closed-set, and open-set scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…In all our experiments, we used the ASVspoof 2019 dataset described in [16,17]. This dataset has been proposed to evaluate a wide variety of tasks related to speech verification, from spoofing detection to countermeasures to replay attacks.…”

Section: Datasetmentioning

confidence: 99%

“…The training set D tr contains bona fide speech from 20 (8 male, 12 female) subjects and synthetic speech generated from 6 methods (i.e., from A01 to A06 using the convention proposed in [17]). The development set D dev contains bona fide speech from 10 (4 male, 6 female) subjects and synthetic speech generated with the same 6 methods used in D tr (i.e., from A01 to A06).…”

Section: Datasetmentioning

confidence: 99%

“…Indeed, some make use of vocoders, others of waveform concatenation, and many others of NN. In the following, a brief description of each one of them [17]:…”

Section: Datasetmentioning

confidence: 99%

“…In order to validate the proposed method on multiple kinds of synthetically generated speech signals, we performed an extensive set of analyses on the publicly available ASVspoof 2019 dataset [16,17]. This dataset contains synthetic speech tracks generated through 17 different speech synthesis techniques, ranging from the older (e.g., waveform concatenation, vocoders, etc) to novel ones based on CNNs approaches.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic speech detection through short-term and long-term prediction traces

Borrelli

Bestagini

Antonacci

et al. 2021

EURASIP J. on Info. Security

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Several methods for synthetic audio speech generation have been developed in the literature through the years. With the great technological advances brought by deep learning, many novel synthetic speech techniques achieving incredible realistic results have been recently proposed. As these methods generate convincing fake human voices, they can be used in a malicious way to negatively impact on today’s society (e.g., people impersonation, fake news spreading, opinion formation). For this reason, the ability of detecting whether a speech recording is synthetic or pristine is becoming an urgent necessity. In this work, we develop a synthetic speech detector. This takes as input an audio recording, extracts a series of hand-crafted features motivated by the speech-processing literature, and classify them in either closed-set or open-set. The proposed detector is validated on a publicly available dataset consisting of 17 synthetic speech generation algorithms ranging from old fashioned vocoders to modern deep learning solutions. Results show that the proposed method outperforms recently proposed detectors in the forensics literature.

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Section: Discussionmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

“…Indeed, some make use of vocoders, others of waveform concatenation, and many others of NN. In the following, a brief description of each one of them [17]:…”

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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