Real-Time Arabic Digit Spotting with TinyML-Optimized CNNs on Edge Devices

TinyML is a rapidly evolving field at the intersection of machine learning and embedded systems. This paper describes and evaluates a TinyML-optimized convolutional neural network (CNN) for realtime digit spotting in the Arabic language when executed on three different computational platforms. The proposed system is designed to recognize a set of Arabic digits from a continuous audio stream in real-time, enabling the development of intelligent voice-activated applications on edge devices. Our results show that our TinyML-optimized CNN model can achieve 90%-93% inference accuracy, within 0.06-38 ms, while occupying only 19-139 KB of memory. These results demonstrate the feasibility of deploying a CNN-based Arabic digit spotting system on resource-constrained edge devices. They also provide insights into the trade-offs between performance and resource utilization on different hardware platforms. This work has important implications for the development of intelligent voice-activated applications in the Arabic language on edge devices, which enables new opportunities for real-time speech processing at the edge.