ATMM-SAGA: Alternating Training for Multi-Module with Score-Aware Gated Attention SASV system
On the map: Speech and audio language models → Anti-spoofing and adversarial audio attacks
Abstract (arXiv)
The objective of automatic speaker verification (ASV) systems is to determine whether a given test speech utterance corresponds to a claimed enrolled speaker. These systems have a wide range of applications, and ensuring their reliability is crucial. In this paper, we propose a spoofing-robust automatic speaker verification (SASV) system employing a score-aware gated attention (SAGA) fusion scheme, integrating scores from a pre-trained countermeasure (CM) with speaker embeddings from a pre-trained ASV. Specifically, we employ the AASIST and ECAPA-TDNN models. SAGA acts as an adaptive gating mechanism, where the CM score determines how strongly ASV embeddings influence the final SASV decision. Experiments on the ASVspoof2019 logical access dataset demonstrate that the proposed SASV system achieves an SASV equal error rate (SASV-EER) and agnostic detection cost function (a-DCF) of 2.31%, 0.0603 for the development set and 2.18%, 0.0480 for the evaluation set.
Key facts. Cited by 2 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 11 of its 19 references that are in the map. First posted on arXiv 23 May 2025.
Builds on
11 of 19 references.
Closest ancestors · 4
Same direction, newest first
- 2024AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge30 Aug
- 2024Generalizing Speaker Verification for Spoof Awareness in the Embedding Space20 Jan
- 2022On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification1 Sep
- 2021End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection27 Jul
Other references · 7
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 980
- 2021AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networkscited by 238
- 2022SASV 2022: The First Spoofing-Aware Speaker Verification Challengecited by 31
- 2023Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verificationcited by 20
- 2024a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verificationcited by 17
- 2022A Probabilistic Fusion Framework for Spoofing Aware Speaker Verificationcited by 11
- 2024To what extent can ASV systems naturally defend against spoofing attacks?cited by 5
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2022Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022
- 2025Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
- 2022Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
- 2022Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
- 2026Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR
- 2022SA-SASV: An End-to-End Spoof-Aggregated Spoofing-Aware Speaker Verification System
- 2022Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion
- 2022SASV Based on Pre-trained ASV System and Integrated Scoring Module