mlsanityData to 7 Oct 2026

"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World

arXiv 2109.09598 ↗First posted 20 Sep 2021cs.CR cs.AI cs.SDcited by 12

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Abstract (arXiv)

Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in the wrong hands will enable a range of powerful attacks against both humans and software systems (aka machines). This paper documents efforts and findings from a comprehensive experimental study on the impact of deep-learning based speech synthesis attacks on both human listeners and machines such as speaker recognition and voice-signin systems. We find that both humans and machines can be reliably fooled by synthetic speech and that existing defenses against synthesized speech fall short. These findings highlight the need to raise awareness and develop new protections against synthetic speech for both humans and machines.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 21 of its 91 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

21 of 91 references.

References · 21

  1. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  2. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
  3. 2017Generalized End-to-End Loss for Speaker Verificationcited by 290
  4. 2018Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesiscited by 280
  5. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
  6. 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
  7. 2018Neural Voice Cloning with a Few Samplescited by 100
  8. 2018Hierarchical Generative Modeling for Controllable Speech Synthesiscited by 93
  9. 2020Unsupervised Speech Decomposition via Triple Information Bottleneckcited by 87
  10. 2018StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networkscited by 78
  11. 2019Who is Real Bob? Adversarial Attacks on Speaker Recognition Systemscited by 66
  12. 2015End-to-End Text-Dependent Speaker Verificationcited by 49
9 more
  1. 2018Fooling End-to-end Speaker Verification by Adversarial Examplescited by 39
  2. 2020VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecturecited by 34
  3. 2019Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversioncited by 29
  4. 2020DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voicescited by 24
  5. 2020Defending Your Voice: Adversarial Attack on Voice Conversioncited by 23
  6. 2020Speech Synthesis using EEGcited by 8
  7. 2020ConVoice: Real-Time Zero-Shot Voice Style Transfer with Convolutional Networkcited by 6
  8. 2019Voice Mimicry Attacks Assisted by Automatic Speaker Verificationcited by 4
  9. 2013An audio CAPTCHA to distinguish humans from computerscited by 3

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Cited by year

Citing papers per year: 2022: 5; 2023: 3; 2024: 2; 2025: 2.

12 papers.