Synthetic speech detection using meta-learning with prototypical loss

01/24/2022
by   Monisankha Pal, et al.
0

Recent works on speech spoofing countermeasures still lack generalization ability to unseen spoofing attacks. This is one of the key issues of ASVspoof challenges especially with the rapid development of diverse and high-quality spoofing algorithms. In this work, we address the generalizability of spoofing detection by proposing prototypical loss under the meta-learning paradigm to mimic the unseen test scenario during training. Prototypical loss with metric-learning objectives can learn the embedding space directly and emerges as a strong alternative to prevailing classification loss functions. We propose an anti-spoofing system based on squeeze-excitation Residual network (SE-ResNet) architecture with prototypical loss. We demonstrate that the proposed single system without any data augmentation can achieve competitive performance to the recent best anti-spoofing systems on ASVspoof 2019 logical access (LA) task. Furthermore, the proposed system with data augmentation outperforms the ASVspoof 2021 challenge best baseline both in the progress and evaluation phase of the LA task. On ASVspoof 2019 and 2021 evaluation set LA scenario, we attain a relative 68.4 to the challenge best baselines, respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/04/2022

SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing

Voice anti-spoofing systems are crucial auxiliaries for automatic speake...
research
10/20/2021

A Study On Data Augmentation In Voice Anti-Spoofing

In this paper, we perform an in-depth study of how data augmentation tec...
research
07/13/2019

Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge

In this paper, we present the system description of the joint efforts of...
research
11/17/2022

Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning

Automatic speaker verification systems are vulnerable to a variety of ac...
research
09/15/2023

One-Class Knowledge Distillation for Spoofing Speech Detection

The detection of spoofing speech generated by unseen algorithms remains ...
research
07/05/2019

The DKU Replay Detection System for the ASVspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, and Fusion

This paper describes our DKU replay detection system for the ASVspoof 20...
research
10/19/2022

Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders

A good training set for speech spoofing countermeasures requires diverse...

Please sign up or login with your details

Forgot password? Click here to reset