# Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis

arXiv: https://arxiv.org/abs/2406.10836 · HTML: https://mlsanity.com/paper/2406.10836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection](https://mlsanity.com/paper/2603.20686/) (2026)
- [RAT: Reference-Augmented Training for ASV Anti-Spoofing](https://mlsanity.com/paper/2606.10908/) (2026)
- [From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection](https://mlsanity.com/paper/2609.08899/) (2026)

## Abstract (arXiv)

Fusing outputs from automatic speaker verification (ASV) and spoofing countermeasure (CM) is expected to make an integrated system robust to zero-effort imposters and synthesized spoofing attacks. Many score-level fusion methods have been proposed, but many remain heuristic. This paper revisits score-level fusion using tools from decision theory and presents three main findings. First, fusion by summing the ASV and CM scores can be interpreted on the basis of compositional data analysis, and score calibration before fusion is essential. Second, the interpretation leads to an improved fusion method that linearly combines the log-likelihood ratios of ASV and CM. However, as the third finding reveals, this linear combination is inferior to a non-linear one in making optimal decisions. The outcomes of these findings, namely, the score calibration before fusion, improved linear fusion, and better non-linear fusion, were found to be effective on the SASV challenge database.