TinySV: Speaker Verification in TinyML with On-device Learning

• URL: http://arxiv.org/abs/2406.01655v1
• Date: Mon, 3 Jun 2024 17:27:40 GMT
• Title: TinySV: Speaker Verification in TinyML with On-device Learning
• Authors: Massimo Pavan, Gioele Mombelli, Francesco Sinacori, Manuel Roveri,
• Abstract summary: This paper introduces a new type of adaptive TinyML solution that can be used in tasks, such as the presented textitTiny Speaker Verification (TinySV) The proposed TinySV solution relies on a two-layer hierarchical TinyML solution comprising Keyword Spotting and Adaptive Speaker Verification module. We evaluate 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.
• Score: 2.356162747014486
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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).

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