Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector

TL;DR

This paper introduces two novel back-end systems, MSFM and IEP, that effectively combine speaker verification and spoofing countermeasures, significantly improving spoofing-aware speaker verification performance.

Contribution

The paper presents two new methods, MSFM and IEP, for integrating ASV and spoofing countermeasures, advancing spoofing-aware speaker verification techniques.

Findings

Achieved SASV EER of 0.56% and 1.32% on SASV 2022 challenge data.

Effectively integrated ASV and CM systems using proposed methods.

Demonstrated significant performance improvements over existing approaches.

Abstract

The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neutralized by spoofing attacks. Therefore, the Spoofing-Aware Speaker Verification (SASV) challenge is designed and held to promote development of systems that can perform ASV considering spoofing attacks by integrating ASV and spoofing countermeasure (CM) systems. In this paper, we propose two back-end systems: multi-layer perceptron score fusion model (MSFM) and integrated embedding projector (IEP). The MSFM, score fusion back-end system, derived SASV score utilizing ASV and CM scores and embeddings. On the other hand,IEP combines ASV and CM embeddings into SASV embedding and calculates final SASV score based on the cosine similarity. We effectively integrated ASV and CM systems through proposed MSFM and IEP and achieved the SASV equal error rates 0.56%, 1.32% on the official evaluation trials of the SASV 2022 challenge.