Tracking the Trend in How Speech Synthesizers Deceive People

2026-08-20

AI Summary

研究探讨了现代语音合成器欺骗性，通过对比不同年份的合成工具及与预训练检测器比较，发现人类感知在识别深度伪造音频上不可靠，需采用程序验证等方法。

Abstract

Advances in speech synthesis have made deepfake audio highly realistic. Earlier studies reported 70-80% human detection accuracy, but relied primarily on older synthesizers. We compare human detection for three selected voice synthesis tools released in 2019, 2022, and 2024 with 82 IT professionals, and benchmark humans against six pretrained detectors on the same material. For fully synthetic speech (full spoofs), the F1 score drops from about 90% for RTVC and YourTTS to 48% for ElevenLabs, although listeners were explicitly warned that deepfakes were present. For partial spoofing, where only one sentence of an utterance is altered, strict accuracy falls to 9%, and listeners classify the synthetic sentence as bona fide 77% of the time. Humans and detectors fail in complementary ways, and neither reliably localizes short manipulations. Additionally, listeners increasingly mislabel bona fide speech as fake, eroding trust in unmanipulated audio. These findings show that human perception alone is unreliable for the selected modern and partial-spoof conditions and motivate procedural verification, provenance, watermarking, and segment-level detection.

Problem

• speech synthesis
• deepfake audio
• human detection accuracy
• partial spoofing
• F1 score

Innovation

• speech synthesis
• deepfake audio
• human detection accuracy
• partial spoofing
• procedural verification