arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve. Yet, most detection models are trained to make inference on frame-level audio features alone without leveraging valuable linguistic cues at larger timescales. To address this gap, we present Linguistically Augmented Audio Speech Data (LinguAS), a dataset of genuine and deepfaked audio samples annotated with five strategically-chosen, Expert-Defined Linguistic Features (EDLFs) t
The proliferation of deepfaked audio necessitates more sophisticated detection mechanisms that go beyond simple acoustic features.
This development enhances the accuracy of distinguishing genuine from synthetic speech, critical for mitigating disinformation and maintaining trust in digital communication.
AI models for deepfake audio detection will now incorporate linguistic features, leading to more robust and reliable classification.
- · Cybersecurity companies
- · Social media platforms
- · Law enforcement agencies
- · AI ethics research
- · Deepfake creators
- · Disinformation campaigns
Improved detection capabilities will make it harder to produce and spread convincing audio deepfakes.
This might drive deepfake creators to develop even more advanced synthesis methods that also mimic linguistic nuances.
A continuous arms race between deepfake generation and detection could necessitate new regulatory frameworks for AI-generated content.
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Read at arXiv cs.LG