# Speaker-Aware Anti-Spoofing

arXiv: https://arxiv.org/abs/2303.01126 · HTML: https://mlsanity.com/paper/2303.01126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

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

- [RAT: Reference-Augmented Training for ASV Anti-Spoofing](https://mlsanity.com/paper/2606.10908/) (2026)

## Abstract (arXiv)

We address speaker-aware anti-spoofing, where prior knowledge of the target speaker is incorporated into a voice spoofing countermeasure (CM). In contrast to the frequently used speaker-independent solutions, we train the CM in a speaker-conditioned way. As a proof of concept, we consider speaker-aware extension to the state-of-the-art AASIST (audio anti-spoofing using integrated spectro-temporal graph attention networks) model. To this end, we consider two alternative strategies to incorporate target speaker information at the frame and utterance levels, respectively. The experimental results on a custom protocol based on ASVspoof 2019 dataset indicates the efficiency of the speaker information via enrollment: we obtain maximum relative improvements of 25.1% and 11.6% in equal error rate (EER) and minimum tandem detection cost function (t-DCF) over a speaker-independent baseline, respectively.