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Short-Term Perceptual Training Modulates Neural Responses to Deepfake Speech But Does Not Improve Behavioral

Jinghan Yang1,2,3, Haoran Jiang1,2,3, Yanru Bai1,2,3

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.

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Short-term training helps the brain detect artificial intelligence (AI)-generated speech, even when people can't consciously tell the difference. This neural sensitivity offers a path to identifying synthetic voices and combating deception.

Keywords:
deepfake perceptionelectroencephalography (EEG)learning and memoryperceptual learningsynthetic voicesvoice recognition

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Area of Science:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Speech Processing

Background:

  • Advancements in artificial intelligence (AI) text-to-speech (TTS) create voices nearly indistinguishable from humans.
  • This realism poses societal risks, including fraud and deception through impersonation.
  • Current methods struggle to reliably differentiate AI voices from human speech.

Purpose of the Study:

  • To investigate if short-term perceptual training enhances the ability to distinguish AI-generated from human speech.
  • To examine behavioral and neural responses to AI and human voices before and after training.
  • To understand the neural-behavioral dissociation in recognizing synthetic speech.

Main Methods:

  • Thirty participants completed behavioral voice discrimination tasks before and after a ~12-minute perceptual training session.
  • Electroencephalography (EEG) recorded neural activity during speech perception.
  • Temporal Response Function (TRF) analysis was used to assess neural differentiation between speech types.

Main Results:

  • Behavioral discrimination showed minimal improvement post-training.
  • Neural analysis revealed significant differentiation between AI and human speech at early and later auditory processing stages after training.
  • EEG spectral power and decoding analyses showed limited differentiation.

Conclusions:

  • A dissociation exists between behavioral and neural sensitivity to AI-generated speech.
  • The auditory system rapidly adapts to subtle acoustic differences after brief exposure, despite poor behavioral performance.
  • Findings highlight the potential for targeted perceptual training to improve synthetic voice detection and mitigate societal risks.