Every Breath You Don't Take: Deepfake Speech Detection Using Breath

TL;DR

This paper introduces a novel breath-based detector for deepfake speech that outperforms complex models, demonstrating breath as a key indicator of natural speech authenticity.

Contribution

The paper presents a simple breath detection method for deepfake speech, creating a new dataset and showing its effectiveness over state-of-the-art deep learning models.

Findings

Breath-based detection achieves perfect AUPRC and zero EER on test data.

Complex SSL-wav2vec model fails to classify in-the-wild deepfakes effectively.

Public dataset facilitates future research in deepfake detection.

Abstract

Deepfake speech represents a real and growing threat to systems and society. Many detectors have been created to aid in defense against speech deepfakes. While these detectors implement myriad methodologies, many rely on low-level fragments of the speech generation process. We hypothesize that breath, a higher-level part of speech, is a key component of natural speech and thus improper generation in deepfake speech is a performant discriminator. To evaluate this, we create a breath detector and leverage this against a custom dataset of online news article audio to discriminate between real/deepfake speech. Additionally, we make this custom dataset publicly available to facilitate comparison for future work. Applying our simple breath detector as a deepfake speech discriminator on in-the-wild samples allows for accurate classification (perfect 1.0 AUPRC and 0.0 EER on test data) across 33.6 hours of audio. We compare our model with the state-of-the-art SSL-wav2vec model and show that this complex deep learning model completely fails to classify the same in-the-wild samples (0.72 AUPRC and 0.99 EER).