Can Emotion Fool Anti-spoofing?

Aurosweta Mahapatra; Ismail Rasim Ulgen; Abinay Reddy Naini; Carlos Busso; Berrak Sisman

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arXiv · 2505.23962

Can Emotion Fool Anti-spoofing?

Aurosweta Mahapatra·Ismail Rasim Ulgen·Abinay Reddy Naini·Carlos Busso·Berrak Sisman

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Abstract

Traditional anti-spoofing focuses on models and datasets built on synthetic speech with mostly neutral state, neglecting diverse emotional variations. As a result, their robustness against high-quality, emotionally expressive synthetic speech is uncertain. We address this by introducing EmoSpoof-TTS, a corpus of emotional text-to-speech samples. Our analysis shows existing anti-spoofing models struggle with emotional synthetic speech, exposing risks of emotion-targeted attacks. Even trained on emotional data, the models underperform due to limited focus on emotional aspect and show performance disparities across emotions. This highlights the need for emotion-focused anti-spoofing paradigm in both dataset and methodology. We propose GEM, a gated ensemble of emotion-specialized models with a speech emotion recognition gating network. GEM performs effectively across all emotions and neutral state, improving defenses against spoofing attacks. We release the EmoSpoof-TTS Dataset: https://emospoof-tts.github.io/Dataset/

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Aurosweta Mahapatra, Ismail Rasim Ulgen, Abinay Reddy Naini, Carlos Busso, Berrak Sisman. 2025-05-29. Can Emotion Fool Anti-spoofing?. https://arxiv.org/abs/2505.23962

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