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).

翻译：TinyML是机器学习的一个新兴领域，得益于在微型设备（如物联网或嵌入式系统）上执行机器学习算法的能力，该领域在过去几年获得了巨大发展。值得注意的是，该领域的研究主要集中在微型设备上高效执行TinyML模型的推理阶段，而由于学习算法引入的相关开销，文献中可用的TinyML模型设备端学习解决方案非常少。本文旨在介绍一种新型自适应TinyML解决方案，该方案可用于需要设备端学习算法处理的任务，例如本文提出的\textit{微型说话人验证}（TinySV）。实现这一目标需要（i）降低TinyML学习算法的内存和计算需求，以及（ii）设计一种能够使用少量且可能无标签训练数据运行的TinyML学习算法。所提出的TinySV解决方案依赖于一个包含关键词检测和自适应说话人验证模块的两层分层TinyML架构。我们在专门为该任务收集的数据集上评估了所提TinySV解决方案的有效性和效率，并在真实物联网设备（Infineon PSoC 62S2 Wi-Fi BT Pioneer Kit）上测试了该方案。