  Speaker identification (SI) determines a speaker's identity based on their
spoken utterances. Previous work indicates that SI deep neural networks (DNNs)
are vulnerable to backdoor attacks. Backdoor attacks involve embedding hidden
triggers in DNNs' training data, causing the DNN to produce incorrect output
when these triggers are present during inference. This is the first work that
explores SI DNNs' vulnerability to backdoor attacks using speakers' emotional
prosody, resulting in dynamic, inconspicuous triggers. %Such an attack could
have real-world implications in forensics, authentication, and surveillance. We
conducted a parameter study using three different datasets and DNN
architectures to determine the impact of emotions as backdoor triggers on the
accuracy of SI systems. Additionally, we have explored the robustness of our
attacks by applying defenses like pruning, STRIP-ViTA, and three popular
preprocessing techniques: quantization, median filtering, and squeezing. Our
findings show that the aforementioned models are prone to our attack,
indicating that emotional triggers (sad and neutral prosody) can be effectively
used to compromise the integrity of SI systems. However, the results of our
pruning experiments suggest potential solutions for reinforcing the models
against our attacks, decreasing the attack success rate up to 40%.
