Abstract
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 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.
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Notes
- 1.
The dialect spoken in Arab states around the Persian Gulf.
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Abu Adla, Y., Saghir, M.A.R., Awad, M. (2023). Real-Time Arabic Digit Spotting with TinyML-Optimized CNNs on Edge Devices. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_44
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