Souza Leonardo Mendes de, Guido Rodrigo Capobianco, Contreras Rodrigo Colnago, Viana Monique Simplicio, Bongarti Marcelo Adriano Dos Santos
Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil.
Department of Science and Technology, Federal University of São Paulo, São José dos Campos 12247-014, SP, Brazil.
Sensors (Basel). 2025 Aug 5;25(15):4821. doi: 10.3390/s25154821.
Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats.
语音生物识别系统在众多安全应用中发挥着关键作用，包括电子设备认证、银行交易验证和机密通信。尽管这些系统具有广泛的实用性，但它们越来越多地成为复杂欺骗攻击的目标，这些攻击利用先进的人工智能技术生成逼真的合成语音。因此，解决基于语音的认证系统固有的漏洞变得既紧迫又至关重要。本研究提出了一种新颖的实验分析方法，该方法广泛探索各种降维策略，并结合监督机器学习模型，以有效识别被欺骗的语音信号。我们的框架包括提取多谱特征，然后应用各种降维方法，如主成分分析（PCA）、截断奇异值分解（SVD）、统计特征选择（方差分析F值、互信息）、递归特征消除（RFE）、基于正则化的套索选择、随机森林特征重要性和排列重要性技术。使用ASVSpoof 2017 v2.0数据集进行的实证评估以等错误率（EER）指标衡量分类性能，得到的值约为10%。我们的比较分析表明，应用降维方法时性能有显著提升，突出了它们在增强语音生物识别验证系统抵御新出现的欺骗威胁的安全性和有效性方面的价值。
