ASVtorch toolkit: Speaker verification with deep neural networks

被引：4

作者：

Lee, Kong Aik ^{[1
]}

Vestman, Ville ^{[2
]}

Kinnunen, Tomi ^{[2
]}

机构：

[1] ASTAR, Inst Infocomm Res, Singapore, Singapore

[2] Univ Eastern Finland, Computat Speech Grp, Joensuu, Finland

来源：

SOFTWAREX | 2021年 / 14卷

基金：

芬兰科学院;

关键词：

Speaker recognition; PyTorch; Deep learning; RECOGNITION;

D O I：

10.1016/j.softx.2021.100697

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The human voice differs substantially between individuals. This facilitates automatic speaker verification (ASV) - recognizing a person from his/her voice. ASV accuracy has substantially increased throughout the past decade due to recent advances in machine learning, particularly deep learning methods. An unfortunate downside has been substantially increased complexity of ASV systems. To help non experts to kick-start reproducible ASV development, a state-of-the-art toolkit implementing various ASV pipelines and functionalities is required. To this end, we introduce a new open-source toolkit, ASVtorch, implemented in Python using the widely used PyTorch machine learning framework. (C) 2021 The Author(s). Published by Elsevier B.V.

引用

页数：6

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