Impact of Channel Variation on One-Class Learning for Spoof Detection
- URL: http://arxiv.org/abs/2109.14900v1
- Date: Thu, 30 Sep 2021 07:56:16 GMT
- Title: Impact of Channel Variation on One-Class Learning for Spoof Detection
- Authors: Rohit Arora, Aanchan Mohan, Saket Anand
- Abstract summary: Spoofing detection increases the reliability of the ASV system but degrades significantly due to channel variation.
"Which data-feeding strategy is optimal for MCT?" is not known in the case of spoof detection.
This study highlights the relevance of the deemed-of-low-importance process of data-feeding and mini-batching to raise awareness of the need to refine it for better performance.
- Score: 5.549602650463701
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The value of Spoofing detection in increasing the reliability of the ASV
system is unparalleled. In reality, however, the performance of countermeasure
systems (CMs) degrades significantly due to channel variation.
Multi-conditional training(MCT) is a well-established technique to handle such
scenarios. However, "which data-feeding strategy is optimal for MCT?" is not
known in the case of spoof detection. In this paper, various codec simulations
were used to modify ASVspoof 2019 dataset, and assessments were done using
data-feeding and mini-batching strategies to help address this question. Our
experiments aim to test the efficacy of the various margin-based losses for
training Resnet based models with LFCC front-end feature extractor to correctly
classify the spoofed and bonafide samples degraded using codec simulations.
Contrastingly to most of the works that focus mainly on architectures, this
study highlights the relevance of the deemed-of-low-importance process of
data-feeding and mini-batching to raise awareness of the need to refine it for
better performance.
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