An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149347" target="_blank" >RIV/00216305:26230/23:PU149347 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10022775" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10022775</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SLT54892.2023.10022775" target="_blank" >10.1109/SLT54892.2023.10022775</a>

Result language

angličtina

Original language name

An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification

Original language description

In recent years, self-supervised learning paradigm has received extensive attention due to its great success in various down-stream tasks. However, the fine-tuning strategies for adapting those pre-trained models to speaker verification task have yet to be fully explored. In this paper, we analyze several feature extraction approaches built on top of a pre-trained model, as well as regularization and a learning rate scheduler to stabilize the fine-tuning process and further boost performance: multi-head factorized attentive pooling is proposed to factorize the comparison of speaker representations into multiple phonetic clusters. We regularize towards the parameters of the pretrained model and we set different learning rates for each layer of the pre-trained model during fine-tuning. The experimental results show our method can significantly shorten the training time to 4 hours and achieve SOTA performance: 0.59%, 0.79% and 1.77% EER on Vox1-O, Vox1-E and Vox1-H, respectively.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings

ISBN

978-1-6654-7189-3

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

555-562

Publisher name

IEEE Signal Processing Society

Place of publication

Doha

Event location

Doha

Event date

Jan 9, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000968851900075