Abstract

Smart-home environments require secure and reliable user authentication mechanisms to prevent unauthorized access and spoofing attacks. Traditional password- and PIN-based methods remain vulnerable to theft, replay attacks, and credential compromise. To address these challenges, this study proposes an intelligent voice-based authentication and anomaly detection framework for secure smart-home environments. The framework utilizes benchmark ASVspoof 2019 and ASVspoof 2021 datasets containing bona fide and spoofed speech samples. After preprocessing, discriminative acoustic features, including Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Cepstral Coefficients (CQCC), are extracted and provided to a Hybrid CNN-LSTM model for speaker verification. An integrated anomaly detection module further enhances security by identifying replay, spoofing, and synthetic speech attacks. Access is granted only when the input voice is authenticated and classified as non-anomalous. Experimental results demonstrate the effectiveness of the proposed framework, achieving an overall accuracy of 97.2% and a macro-AUC of 0.972. The model also achieves low Equal Error Rates of 3.8%, 2.9%, and 2.1% across the evaluated classes, indicating robust spoof detection and anomaly generalization capabilities. These results highlight the suitability of the proposed framework for secure and intelligent smart-home access control applications.

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