Publication Details
Progressive contrastive learning for self-supervised text-independent speaker verification
self-supervised, text-independent, speaker, verification
Self-supervised speaker representation learning has drawn attention extensively
in recent years. Most of the work is based on the iterative
clustering-classification learning framework, and the performance is sensitive to
the pre-defined number of clusters. However, the cluster number is hard to
estimate when dealing with large-scale unlabeled data. In this paper, we propose
a progressive contrastive learning (PCL) algorithm to dynamically estimate the
cluster number at each step based on the statistical characteristics of the data
itself, and the estimated number will progressively approach the ground-truth
speaker number with the increasing of step. Specifically, we first update the
data queue by current augmented samples. Then, eigendecomposition is introduced
to estimate the number of speakers in the updated data queue. Finally, we assign
the queued data into the estimated cluster centroid and construct a contrastive
loss, which encourages the speaker representation to be closer to its cluster
centroid and away from others. Experimental results on VoxCeleb1 demonstrate the
effectiveness of our proposed PCL compared with existing self-supervised
approaches.
@inproceedings{BUT179661,
author="Junyi {Peng} and Chunlei {Zhang} and Jan {Černocký} and Dong {Yu}",
title="Progressive contrastive learning for self-supervised text-independent speaker verification",
booktitle="Proceedings of The Speaker and Language Recognition Workshop (Odyssey 2022)",
year="2022",
pages="17--24",
publisher="International Speech Communication Association",
address="Beijing",
doi="10.21437/Odyssey.2022-3",
url="https://www.isca-speech.org/archive/pdfs/odyssey_2022/peng22_odyssey.pdf"
}