Tinysv: Speaker Verification In Tinyml With On-device Learning

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 relie

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keypavan2024tinysv

Related papers

• Boosting Keyword Spotting Through On-device Learnable User Speech Characteristics (2024)0.00
• Small Footprint Text-independent Speaker Verification For Embedded Systems (2020)7.16
• Towards Lightweight Speaker Verification Via Adaptive Neural Network Quantization (2024)5.84
• Memory-efficient Training For Deep Speaker Embedding Learning In Speaker Verification (2024)2.26
• Multi-task Metric Learning For Text-independent Speaker Verification (2020)0.00
• Rsknet-mtsp: Effective And Portable Deep Architecture For Speaker Verification (2021)9.03
• Efficient Black-box Speaker Verification Model Adaptation With Reprogramming And Backend Learning (2023)0.00
• Devicetts: A Small-footprint, Fast, Stable Network For On-device Text-to-speech (2020)0.00