Generalizing Speaker Verification for Spoof Awareness in the Embedding Space

Xuechen Liu, Md Sahidullah, Kong Aik Lee, Tomi Kinnunen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

It is now well-known that automatic speaker verification (ASV) systems can be spoofed using various types of adversaries. The usual approach to counteract ASV systems against such attacks is to develop a separate spoofing countermeasure (CM) module to classify speech input either as a bonafide, or a spoofed utterance. Nevertheless, such a design requires additional computation and utilization efforts at the authentication stage. An alternative strategy involves a single monolithic ASV system designed to handle both zero-effort imposter (non-targets) and spoofing attacks. Such spoof-aware ASV systems have the potential to provide stronger protections and more economic computations. To this end, we propose to generalize the standalone ASV (G-SASV) against spoofing attacks, where we leverage limited training data from CM to enhance a simple backend in the embedding space, without the involvement of a separate CM module during the test (authentication) phase. We propose a novel yet simple backend classifier based on deep neural networks and conduct the study via domain adaptation and multi-task integration of spoof embeddings at the training stage. Experiments are conducted on the ASVspoof 2019 logical access dataset, where we improve the performance of statistical ASV backends on the joint (bonafide and spoofed) and spoofed conditions by a maximum of 36.2% and 49.8% in terms of equal error rates, respectively.

Original languageEnglish
Pages (from-to)1261-1273
Number of pages13
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume32
DOIs
Publication statusPublished - Jan 2024

Keywords

  • Anti-spoofing
  • ASVspoof
  • speaker recognition
  • spoof-aware speaker verification (SASV)

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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