Developing a secure voice recognition service on Raspberry Pi - Le, Van-Hoan; Luc, Nhu-Quynh; Quach, Duc-Huy (Institute of Advanced Engineering and Science, 2024)
In this study, we present a novel voice recognition service developed on the Raspberry Pi 4 model B platform, leveraging the fast Fourier transform (FFT) for efficient speech-to-digital signal conversion. By integrating the hidden Markov model (HMM) and artificial neural network (ANN), our system accurately reconstructs speech input. We further fortify this service with dual-layer encryption using the Rivest–Shamir–Adleman (RSA) and advanced encryption standard (AES) methods, achieving encryption and decryption times well suited for real-time applications. Our results demonstrate the system's robustness and efficiency: speech processing within 1.2 to 1.9 seconds, RSA 2048-bit encryption in 2 to 6 milliseconds, RSA decryption in 6 to 10 milliseconds, and AES-GCM 256-bit encryption and decryption in approximately 2.6 to 3 seconds.