# Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System

arXiv: https://arxiv.org/abs/2012.03154 · HTML: https://mlsanity.com/paper/2012.03154/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022](https://mlsanity.com/paper/2209.00423/) (2022)
- [Synthetic Speech Detection Based on Temporal Consistency and Distribution of Speaker Features](https://mlsanity.com/paper/2309.16954/) (2023)

## Abstract (arXiv)

Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types of attacks from synthetic speech to replay presentations. While there are numerous effective defenses reported on standalone anti-spoofing solutions, the integration for speaker verification and spoofing detection systems has obvious benefits. In this paper, we propose a spoofing-robust automatic speaker verification (SR-ASV) system for diverse attacks based on a multi-task learning architecture. This deep learning based model is jointly trained with time-frequency representations from utterances to provide recognition decisions for both tasks simultaneously. Compared with other state-of-the-art systems on the ASVspoof 2017 and 2019 corpora, a substantial improvement of the combined system under different spoofing conditions can be obtained.