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

共 50 条

[41] Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks
Hernandez Sanchez, Sara
Fernandez Pozo, Ruben
Hernandez Gomez, Luis Alfonso
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 97 - 109
[42] Neural Discriminant Analysis for Deep Speaker Embedding
Li, Lantian
Wang, Dong
Zheng, Thomas Fang
INTERSPEECH 2020, 2020, : 3251 - 3255
[43] Deep Face Verification Based Convolutional Neural Network
Ben Fredj, Hana
Bouguezzi, Safa
Souani, Chokri
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2021, 21 (05): : 256 - 266
[44] Dictionary Attacks on Speaker Verification
Marras, Mirko
Korus, Pawel
Jain, Anubhav
Memon, Nasir
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2023, 18 : 773 - 788
[45] Optimizing Multi-Taper Features for Deep Speaker Verification
Liu, Xuechen
Sahidullah, Md
Kinnunen, Tomi
IEEE SIGNAL PROCESSING LETTERS, 2021, 28 : 2187 - 2191
[46] An Investigation of Deep-Learning Frameworks for Speaker Verification Antispoofing
Zhang, Chunlei
Yu, Chengzhu
Hansen, John H. L.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2017, 11 (04) : 684 - 694
[47] Investigating Raw Wave Deep Neural Networks for End-to-End Speaker Spoofing Detection
Dinkel, Heinrich
Qian, Yanmin
Yu, Kai
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2018, 26 (11) : 2002 - 2014
[48] Tandem Deep Features for Text-Dependent Speaker Verification
Fu, Tianfan
Qian, Yanmin
Liu, Yuan
Yu, Kai
15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4, 2014, : 1327 - 1331
[49] Cross-lingual Speaker Verification with Deep Feature Learning
Li, Lantian
Wang, Dong
Rozi, Askar
Zheng, Thomas Fang
2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017), 2017, : 1040 - 1044
[50] Convolutional and Deep Neural Networks based techniques for extracting the age-relevant features of the speaker
Kuppusamy, Karthika
Eswaran, Chandra
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2021, 13 (12) : 5655 - 5667

← 1 2 3 4 5 →