# Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification

arXiv: https://arxiv.org/abs/2509.12668 · HTML: https://mlsanity.com/paper/2509.12668/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Placed in

- Speech and audio language models / [Anti-spoofing and adversarial audio attacks](https://mlsanity.com/speech/anti-spoofing-and-adversarial-audio-attacks/)

## Builds on (most cited first, 13 of 13)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2106.15793/) (2021)
- [a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification](https://mlsanity.com/paper/2403.01355/) (2024)
- [Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion](https://mlsanity.com/paper/2204.09976/) (2022)
- [A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification](https://mlsanity.com/paper/2202.05253/) (2022)
- [The DKU-OPPO System for the 2022 Spoofing-Aware Speaker Verification Challenge](https://mlsanity.com/paper/2207.07510/) (2022)
- [Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition](https://mlsanity.com/paper/2002.11312/) (2020)
- [On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification](https://mlsanity.com/paper/2209.00506/) (2022)
- [Spoofing-Aware Speaker Verification by Multi-Level Fusion](https://mlsanity.com/paper/2203.15377/) (2022)

## Built on by (most cited first, 1 of 1)

- [Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification](https://mlsanity.com/paper/2510.01818/) (2025)

## Abstract (arXiv)

Despite improvements in automatic speaker verification (ASV), vulnerability against spoofing attacks remains a major concern. In this study, we investigate the integration of ASV and countermeasure (CM) subsystems into a modular spoof-aware speaker verification (SASV) framework. Unlike conventional single-stage score-level fusion methods, we explore the potential of a multi-stage approach that utilizes the ASV and CM systems in multiple stages. By leveraging ECAPA-TDNN (ASV) and AASIST (CM) subsystems, we consider support vector machine and logistic regression classifiers to achieve SASV. In the second stage, we integrate their outputs with the original score to revise fusion back-end classifiers. Additionally, we incorporate another auxiliary score from RawGAT (CM) to further enhance our SASV framework. Our approach yields an equal error rate (EER) of 1.30% on the evaluation dataset of the SASV2022 challenge, representing a 24% relative improvement over the baseline system.