# SASV Based on Pre-trained ASV System and Integrated Scoring Module

arXiv: https://arxiv.org/abs/2207.00150 · HTML: https://mlsanity.com/paper/2207.00150/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 1 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification](https://mlsanity.com/paper/2209.00506/) (2022)
- [Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification](https://mlsanity.com/paper/2510.01818/) (2025)
- [Synthetic Speech Detection Based on Temporal Consistency and Distribution of Speaker Features](https://mlsanity.com/paper/2309.16954/) (2023)

## Abstract (arXiv)

Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge. The training and scoring of ASV and anti-spoofing countermeasure (CM) in current SASV systems are relatively independent, ignoring the correlation. In this paper, by leveraging the correlation between the two tasks, an integrated SASV system can be obtained by simply training a few more layers on the basis of the baseline pre-trained ASV subsystem. The features in pre-trained ASV system are utilized for logical access spoofing speech detection. Further, speaker embeddings extracted by the pre-trained ASV system are used to improve the performance of the CM. The integrated scoring module takes the embeddings of the ASV and anti-spoofing branches as input and preserves the correlation between the two tasks through matrix operations to produce integrated SASV scores. Submitted primary system achieved equal error rate (EER) of 3.07% on the development dataset of the SASV 2022 Challenge and 4.30% on the evaluation part, which is a 25% improvement over the baseline systems.