Deep learning methods in speaker recognition: a review
Abstract
This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5-6 years. However, as deep learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in speaker recognition too. It seems that DL becomes the now state-of-the-art solution for both speaker verification and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to DL, where they are the most effective.
Keywords
Cite
@article{arxiv.1911.06615,
title = {Deep learning methods in speaker recognition: a review},
author = {Dávid Sztahó and György Szaszák and András Beke},
journal= {arXiv preprint arXiv:1911.06615},
year = {2022}
}