Lightweight Voice Authentication for IoT Devices Using MFCC and CNN on Edge Hardware

Abstract: This paper presents a lightweight, real-time voice authentication system designed for IoT devices to enhance security by allowing only authorized voice commands. It utilizes a MEMS-based INMP441 microphone and an ESP32-S3 microcontroller to capture audio, extract features via Mel-Frequency Cepstral Coefficients (MFCC), and classify them using a compact Convolutional Neural Network (CNN). Trained with TensorFlow and deployed with TensorFlowLite, the system supports efficient on-device inference, ideal for resource-limited edge hardware.Combining signal processing with deep learning, the solution ensures low latency, minimal power consumption, and enhanced privacy by performing all processing locally—avoiding cloud dependency. It demonstrates robust performance in diverse acoustic conditions and is well-suited for applications in smart homes, healthcare, and industrial automation.This work highlights the viability of embedded AI for secure, intuitive voice interfaces in IoT. Future improvements may include adaptive learning, multi-user support, and integration with other biometric modalities.