DeepAI
Log In Sign Up

Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes

08/29/2018
by   Hye-Jin Shim, et al.
0

In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30 evaluation set.

READ FULL TEXT
08/29/2018

Replay attack spoofing detection system using replay noise by multi-task learning

In this paper, we propose a spoofing detection system for replay attack ...
06/10/2020

Integrated Replay Spoofing-aware Text-independent Speaker Verification

A number of studies have successfully developed speaker verification or ...
01/31/2020

A study on the role of subsidiary information in replay attack spoofing detection

In this study, we analyze the role of various categories of subsidiary i...
02/16/2020

Multi-Task Siamese Neural Network for Improving Replay Attack Detection

Automatic speaker verification systems are vulnerable to audio replay at...
05/24/2017

Audio-replay attack detection countermeasures

This paper presents the Speech Technology Center (STC) replay attack det...
10/22/2019

Self-supervised pre-training with acoustic configurations for replay spoofing detection

Large datasets are well-known as a key to the recent advances in deep le...