DR-SASV: A deep and reliable spoof aware speech verification system

Abstract

A spoof-aware speaker verification system is an integrated system that is capable of jointly identifying impostor speakers as well as spoofing attacks from target speakers. This type of system largely helps in protecting sensitive data, mitigating fraud, and reducing theft. Research has recently enhanced the effectiveness of countermeasure systems and automatic speaker verification systems separately to produce low Equal Error Rates (EER) for each system. However, work exploring a combination of both is still scarce. This paper proposes an end-to-end solution to address spoof-aware automatic speaker verification (ASV) by introducing a Deep Reliable Spoof-Aware-Speaker-Verification (DR-SASV) system. The proposed system allows the target audio to pass through a “spoof aware” speaker verification model sequentially after applying a convolutional neural network (CNN)-based spoof detection model. The suggested system produces encouraging results after being trained on the ASVSpoof 2019 LA dataset. The spoof detection model gives a validation accuracy of 96%, while the transformer-based speech verification model authenticates users with an error rate of 13.74%. The system surpasses other state-of-the-art models and produces an EER score of 10.32%.