Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis
<|reference_start|>Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis: 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.<|reference_end|>
@article{wang2024revisiting,
    title={Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker
  Verification Using Compositional Data Analysis},
    author={Xin Wang, Tomi Kinnunen, Kong Aik Lee, Paul-Gauthier No'e, Junichi
  Yamagishi},
    journal={arXiv preprint arXiv:2406.10836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10836},
    primaryClass={eess.AS cs.SD}
}