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 propose a suitable methodology for audio data processing, feature extraction, and CNN-LSTM architecture. The observed outcomes highlighted the significant vulnerability of CNN-LSTM models to adversarial examples (AEs). In fact, all the considered adversarial attacks are able to significantly reduce the performance of the constructed models. Furthermore, when assessing the efficacy of the attacks, minor differences were noted between the languages analyzed as well as between male and female speech. In summary, this work contributes to the understanding of the robustness of CNN-LSTM models, particularly in SER scenarios, and the impact of AEs. Interestingly, our findings serve as a baseline for a) developing more robust algorithms for SER, b) designing more effective attacks, c) investigating possible defenses, d) improved understanding of the vocal differences between different languages and genders, and e) overall, enhancing our comprehension of the SER task.

翻译：语音情感识别（SER）近年来因其在多个领域的潜在应用价值以及深度学习技术提供的可能性而持续受到关注。然而，近期研究表明深度学习模型可能面临对抗攻击的威胁。本文通过系统评估SER场景中不同语言的男女语音在多种白盒与黑盒对抗攻击下的影响，对该问题进行了全面分析。我们首先提出了一套适用于音频数据处理、特征提取及CNN-LSTM架构的方法论。实验结果表明，CNN-LSTM模型对对抗样本（AEs）存在显著脆弱性——事实上，所有考虑的对抗攻击均能显著降低所构建模型的性能。此外，在评估攻击有效性时，不同语言之间以及男女语音之间的差异较小。总体而言，本研究有助于理解CNN-LSTM模型（尤其在SER场景中）的鲁棒性及对抗样本的影响。值得关注的是，我们的研究结果为以下方面提供了基准参考：a) 开发更鲁棒的SER算法，b) 设计更有效的攻击方法，c) 探索可能的防御策略，d) 加深对不同语言与性别间语音差异的理解，e) 全面提升对SER任务的认知。