TinySV: Speaker Verification in TinyML with On-device Learning
<|reference_start|>TinySV: Speaker Verification in TinyML with On-device Learning: TinyML is a novel area of machine learning that gained huge momentum in the last few years thanks to the ability to execute machine learning algorithms on tiny devices (such as Internet-of-Things or embedded systems). Interestingly, research in this area focused on the efficient execution of the inference phase of TinyML models on tiny devices, while very few solutions for on-device learning of TinyML models are available in the literature due to the relevant overhead introduced by the learning algorithms.   The aim of this paper is to introduce a new type of adaptive TinyML solution that can be used in tasks, such as the presented \textit{Tiny Speaker Verification} (TinySV), that require to be tackled with an on-device learning algorithm. Achieving this goal required (i) reducing the memory and computational demand of TinyML learning algorithms, and (ii) designing a TinyML learning algorithm operating with few and possibly unlabelled training data. The proposed TinySV solution relies on a two-layer hierarchical TinyML solution comprising Keyword Spotting and Adaptive Speaker Verification module. We evaluated the effectiveness and efficiency of the proposed TinySV solution on a dataset collected expressly for the task and tested the proposed solution on a real-world IoT device (Infineon PSoC 62S2 Wi-Fi BT Pioneer Kit).<|reference_end|>
@article{pavan2024tinysv:,
    title={TinySV: Speaker Verification in TinyML with On-device Learning},
    author={Massimo Pavan, Gioele Mombelli, Francesco Sinacori, Manuel Roveri},
    journal={arXiv preprint arXiv:2406.01655},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01655},
    primaryClass={cs.SD cs.LG eess.AS}
}