I Hear, Therefore I Trust: A Socio-Technical Investigation of Humans as Synthetic Speech Detectors
arXiv:2605.28064v1 Announce Type: cross Abstract: Automatic deepfake detection has received considerable research attention, yet the socio-technical environment in which humans actually encounter synthetic speech remains poorly understood. We investigate voice deepfake detection as a perceptual and contextual process, presenting a localization task in which 47 participants marked suspected synthetic segments across authentic, fully synthetic, and partially synthetic utterances under three manipulated trust cues: instructional framing, affective priming, and provenance labeling. Participants pr
The proliferation of sophisticated AI deepfake generation capabilities necessitates a deeper understanding of human detection limitations and vulnerabilities.
Understanding how humans perceive and trust synthetic speech is critical for mitigating misuse of AI, maintaining information integrity, and ensuring secure communication platforms.
This research provides empirical data on human susceptibility to manipulated trust cues in the context of synthetic speech, highlighting the need for robust socio-technical defenses beyond purely automated detection.
- · Cybersecurity firms
- · AI ethics researchers
- · Social media platforms
- · Regulatory bodies
- · Misinformation networks
- · Unsuspecting public
- · AI voice clone developers (if usage is restricted)
Increased investment in multi-modal deepfake detection technologies and public education campaigns.
Development of new verification protocols for audio and voice communication in sensitive sectors.
Potential erosion of trust in digital audio communication, leading to a demand for 'authenticated' human-generated content.
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Read at arXiv cs.AI