# Spoofing-Robust Speaker Verification Using Parallel Embedding Fusion: BTU Speech Group's Approach for ASVspoof5 Challenge

arXiv: https://arxiv.org/abs/2408.15877 · HTML: https://mlsanity.com/paper/2408.15877/

## Key facts

- Cited by 3 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 15 of its 17 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## 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, 15 of 15)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [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)
- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [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)
- [Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector](https://mlsanity.com/paper/2206.13807/) (2022)
- [Generalizing Speaker Verification for Spoof Awareness in the Embedding Space](https://mlsanity.com/paper/2401.11156/) (2024)
- [Backend Ensemble for Speaker Verification and Spoofing Countermeasure](https://mlsanity.com/paper/2207.01802/) (2022)
- [The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge](https://mlsanity.com/paper/2204.01399/) (2022)
- [Optimizing a-DCF for Spoofing-Robust Speaker Verification](https://mlsanity.com/paper/2407.04034/) (2024)

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

- [Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification](https://mlsanity.com/paper/2510.01818/) (2025)
- [Bayesian Learning for Domain-Invariant Speaker Verification and Anti-Spoofing](https://mlsanity.com/paper/2506.07536/) (2025)
- [Joint Optimization of ASV and CM tasks: BTUEF Team's Submission for WildSpoof Challenge](https://mlsanity.com/paper/2602.01722/) (2026)

## Abstract (arXiv)

This paper introduces the parallel network-based spoofing-aware speaker verification (SASV) system developed by BTU Speech Group for the ASVspoof5 Challenge. The SASV system integrates ASV and CM systems to enhance security against spoofing attacks. Our approach employs score and embedding fusion from ASV models (ECAPA-TDNN, WavLM) and CM models (AASIST). The fused embeddings are processed using a simple DNN structure, optimizing model performance with a combination of recently proposed a-DCF and BCE losses. We introduce a novel parallel network structure where two identical DNNs, fed with different inputs, independently process embeddings and produce SASV scores. The final SASV probability is derived by averaging these scores, enhancing robustness and accuracy. Experimental results demonstrate that the proposed parallel DNN structure outperforms traditional single DNN methods, offering a more reliable and secure speaker verification system against spoofing attacks.