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).

介绍了一种新型的自适应 TinyML 解决方案，可用于需要在设备上学习算法的任务，如识别 Tiny Speaker Verification，并在实际的物联网设备上测试了该解决方案。