A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models

arXiv:2404.18514v1 Announce Type: cross
Abstract: Speech emotion recognition (SER) is constantly gaining attention in recent years due to its potential applications in diverse fields and thanks to the possibility offered by deep learning technologies. However, recent studies have shown that deep learning models can be vulnerable to adversarial attacks. In this paper, we systematically assess this problem by examining the impact of various adversarial white-box and black-box attacks on different languages and genders within the context of SER. We first …

abstract adversarial adversarial attacks applications arxiv attacks attention cs.lg cs.sd deep learning diverse eess.as emotion evaluation fields however possibility recognition speech speech emotion studies technologies type vulnerable