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

Autor: Yuanjun Zhao, Victor Sreeram, Roberto Togneri
Rok vydání: 2022
Předmět:
Zdroj: Circuits, Systems, and Signal Processing. 41:4068-4089
ISSN: 1531-5878
0278-081X
Popis: 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 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.
Databáze: OpenAIRE
Externí odkaz: