Spoken Command Recognition System for Hands-Free Door Control - Adetoyi, E. Oluyemi; Folami, Oluwapelumi Precious (Journal of Science, Technology and Innovation Research, 2026)
This paper presents the development of a Spoken Command Recognition System (SCRS) that utilizes a lightweight convolutional neural network (CNN) for hands-free door access control. The system interprets voice commands, specifically "open" and "close", to activate a solenoid that locks or unlocks a door. A dataset comprising 2,000 labeled audio samples (open/close commands) and 70 environmental noise samples was collected. Mel Frequency Cepstral Coefficients (MFCCs) were extracted from the recordings, with 80% used to train the CNN and the remaining 20% reserved for validation. The trained model was quantized using Edge Impulse and deployed on an ESP32-S3 microcontroller using TensorFlow Lite. The deployed system achieved a recognition accuracy of 97.5% with a real-time response time of 3000ms. The SCRS offers a low-power, real-time, and moderately noise-tolerant solution for voice-activated door access. Keywords: Speaker recognition, CNN, embedded systems, ESP32-S3, door access control.