| 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 real-time 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 msec, 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. |
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