Multi-task learning of deep neural networks for joint automatic speaker verification and spoofing detection

2019 ◽  
Author(s):  
Jiakang Li ◽  
Meng Sun ◽  
Xiongwei Zhang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Speaker Verification ◽  
Task Learning ◽  
Spoofing Detection

Rapid adaptation for deep neural networks through multi-task learning

2015 ◽  
Author(s):  
Zhen Huang ◽  
Jinyu Li ◽  
Sabato Marco Siniscalchi ◽  
I-Fan Chen ◽  
Ji Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Rapid Adaptation ◽  
Task Learning

scholarly journals Latent Multi-Task Architecture Learning

2019 ◽  
Vol 33 ◽  
pp. 4822-4829 ◽  
Author(s):  
Sebastian Ruder ◽  
Joachim Bingel ◽  
Isabelle Augenstein ◽  
Anders Søgaard
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Approaches To Learning ◽  
Average Error ◽  
Task Learning ◽  
Task Architecture ◽  
Parameter Sharing

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)–(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15% average error reductions over common approaches to MTL.

Multi-task learning for the prediction of wind power ramp events with deep neural networks

2020 ◽  
Vol 123 ◽  
pp. 401-411 ◽  
Author(s):  
M. Dorado-Moreno ◽  
N. Navarin ◽  
P.A. Gutiérrez ◽  
L. Prieto ◽  
A. Sperduti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wind Power ◽  
Deep Neural Networks ◽  
Task Learning ◽  
Ramp Events ◽  
Wind Power Ramp Events

Dataset-aware multi-task learning approaches for biomedical named entity recognition

2020 ◽  
Vol 36 (15) ◽  
pp. 4331-4338
Author(s):  
Mei Zuo ◽  
Yang Zhang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Quality Data ◽  
Supplementary Information ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. Results We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. Availability and implementation Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BIOMETRIC HUMAN AUTHENTICATION SYSTEM THROUGH SPEECH USING DEEP NEURAL NETWORKS (DNN)

THE BULLETIN ◽  
2020 ◽  
Vol 5 (387) ◽  
pp. 6-15
Author(s):  
O. Mamyrbayev ◽  
◽  
A. Akhmediyarova ◽  
A. Kydyrbekova ◽  
N. O. Mekebayev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Teaching Methods ◽  
Deep Neural Networks ◽  
Speaker Verification ◽  
Noisy Data ◽  
Gaussian Mixture ◽  
Modern Method ◽  
Background Modeling ◽  
Authentication System ◽  
Text Independent Speaker Verification

Biometrics offers more security and convenience than traditional methods of identification. Recently, DNN has become a means of a more reliable and efficient authentication scheme. In this work, we compare two modern teaching methods: these two methods are methods based on the Gaussian mixture model (GMM) (denoted by the GMM i-vector) and methods based on deep neural networks (DNN) (denoted as the i-vector DNN). The results show that the DNN system with an i-vector is superior to the GMM system with an i-vector for various durations (from full length to 5s). DNNs have proven to be the most effective features for text-independent speaker verification in recent studies. In this paper, a new scheme is proposed that allows using DNN when checking text using hints in a simple and effective way. Experiments show that the proposed scheme reduces EER by 24.32% compared with the modern method and is evaluated for its reliability using noisy data, as well as data collected in real conditions. In addition, it is shown that the use of DNN instead of GMM for universal background modeling leads to a decrease in EER by 15.7%.

scholarly journals Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hongwei Luo ◽  
Yijie Shen ◽  
Feng Lin ◽  
Guoai Xu
Keyword(s):  
Neural Networks ◽  
Loss Function ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
The State ◽  
Verification System ◽  
Adversarial Examples ◽  
Human Hearing

Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.

scholarly journals Multi-task learning deep neural networks for speech feature denoising

2015 ◽  
Author(s):  
Bin Huang ◽  
Dengfeng Ke ◽  
Hao Zheng ◽  
Bo Xu ◽  
Yanyan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Task Learning ◽  
Speech Feature
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