# STC Antispoofing Systems for the ASVspoof2019 Challenge

## TL;DR

This paper presents STC's deep learning antispoofing systems for the ASVspoof 2019 challenge, achieving high accuracy in detecting speech synthesis, voice conversion, and replay attacks across different scenarios.

## Contribution

The paper introduces enhanced Light CNN architectures with angular margin softmax for robust antispoofing, demonstrating improved performance over previous methods.

## Key findings

- Achieved 1.86% EER in logical access scenario.
- Achieved 0.54% EER in physical access scenario.
- Demonstrated stability across unknown spoofing attack types.

## Abstract

This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and physical access use-case scenario with replay attacks. During the challenge we developed anti-spoofing solutions for both scenarios. The proposed systems are implemented using deep learning approach and are based on different types of acoustic features. We enhanced Light CNN architecture previously considered by the authors for replay attacks detection and which performed high spoofing detection quality during the ASVspoof2017 challenge. In particular here we investigate the efficiency of angular margin based softmax activation for training robust deep Light CNN classifier to solve the mentioned-above tasks. Submitted systems achieved EER of 1.86% in logical access scenario and 0.54% in physical access scenario on the evaluation part of the Challenge corpora. High performance obtained for the unknown types of spoofing attacks demonstrates the stability of the offered approach in both evaluation conditions.