Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/catching-989-out-of-100-deepfakes-what-it-takes-to-lead-hugging-faces-leaderboard.
Modulate tops Hugging Face's Speech Deepfake Leaderboard with 98.9% accuracy at $0.25/hr. Here's what voice-native architecture unlocks for fraud teams.
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Voice deepfake losses are projected to hit $40B by 2027, a 6,566% jump from 2023. Modulate's velma-2 now ranks #1 on Hugging Face's Speech Deepfake Leaderboard with a 1.104% average EER across 14 datasets and 2M+ audio samples, catching 98.9 out of every 100 deepfakes. This post breaks down why the Hugging Face benchmark is the most credible public standard for detection, how Modulate's voice-native ELM architecture outperforms repurposed models from Hiya and Resemble AI, and why running detection at $0.25/hr (100x cheaper than competitors) lets fraud teams monitor entire calls instead of just the opening seconds where most checks stop today.