Backdoor Attacks on Speech Emotion Recognition via TTS-Generated Poisoning

Abstract:Speech Emotion Recognition (SER) systems increasingly leverage self-supervised acoustic representations, yet their vulnerability to training-time attacks remains largely underexplored. This paper presents the first systematic study of poisoning-based backdoor attacks on SER, with a focus on threats enabled by text-to-speech (TTS) generated audio. We introduce a stealthy, low-energy acoustic trigger that can be embedded imperceptibly into both natural and synthetic speech, enabling scalable and consistent poisoning. Our experiments demonstrate that SER models can be reliably compromised with high attack success rates under low poisoning ratios, while maintaining near-clean performance on benign inputs. We further show that backdoor patterns exhibit strong cross-model transferability and that self-supervised representations are particularly susceptible to learning these triggers. These findings reveal that TTS technology dramatically lowers the barrier to effective backdoor attacks, exposing critical vulnerabilities in modern SER pipelines and motivating the urgent need for dedicated defenses.

Accepted by IEEE Cyber AI 2026. This is the author preprint version

Sound (cs.SD); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)

arXiv:2606.21052 [cs.SD]

(or arXiv:2606.21052v2 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2606.21052 Focus to learn more arXiv-issued DOI via DataCite

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