Publication Details
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification
Plchot Oldřich, Ing., Ph.D. (DCGM)
Stafylakis Themos
Mošner Ladislav, Ing. (DCGM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Pre-trained model, fine-tuning strategy, speaker verification, attentive pooling
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.
@inproceedings{BUT185120,
author="Junyi {Peng} and Oldřich {Plchot} and Themos {Stafylakis} and Ladislav {Mošner} and Lukáš {Burget} and Jan {Černocký}",
title="An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification",
booktitle="2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings",
year="2023",
pages="555--562",
publisher="IEEE Signal Processing Society",
address="Doha",
doi="10.1109/SLT54892.2023.10022775",
isbn="978-1-6654-7189-3",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10022775"
}