Audio Deepfake Detection in the Age of Advanced Text-to-Speech models

• URL: http://arxiv.org/abs/2601.20510v1
• Date: Wed, 28 Jan 2026 11:39:40 GMT
• Title: Audio Deepfake Detection in the Age of Advanced Text-to-Speech models
• Authors: Robin Singh, Aditya Yogesh Nair, Fabio Palumbo, Florian Barbaro, Anna Dyka, Lohith Rachakonda,
• Abstract summary: Recent advances in Text-to-Speech (TTS) systems have substantially increased the realism of synthetic speech.<n>Recent advances in Text-to-Speech (TTS) systems have substantially increased the realism of synthetic speech.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Text-to-Speech (TTS) systems have substantially increased the realism of synthetic speech, raising new challenges for audio deepfake detection. This work presents a comparative evaluation of three state-of-the-art TTS models--Dia2, Maya1, and MeloTTS--representing streaming, LLM-based, and non-autoregressive architectures. A corpus of 12,000 synthetic audio samples was generated using the Daily-Dialog dataset and evaluated against four detection frameworks, including semantic, structural, and signal-level approaches. The results reveal significant variability in detector performance across generative mechanisms: models effective against one TTS architecture may fail against others, particularly LLM-based synthesis. In contrast, a multi-view detection approach combining complementary analysis levels demonstrates robust performance across all evaluated models. These findings highlight the limitations of single-paradigm detectors and emphasize the necessity of integrated detection strategies to address the evolving landscape of audio deepfake threats.

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