Audio-based Kinship Verification Using Age Domain Conversion
Qiyang Sun, Alican Akman, Xin Jing, Manuel Milling, Bj\"orn W., Schuller

TL;DR
This paper introduces an age domain conversion method using CycleGAN-VC3 to improve audio-based kinship verification accuracy by standardizing age-related domain biases.
Contribution
It proposes a novel age-standardized domain approach with CycleGAN-VC3 for age-audio conversion in kinship verification, enhancing accuracy and providing new research insights.
Findings
Significant accuracy improvement in kinship verification
Effective age-standardization reduces domain bias
Potential for future kinship verification advancements
Abstract
Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an "age-standardised domain" wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset, which contains age and kinship labels. The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel…
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Taxonomy
TopicsFace recognition and analysis · Technology Use by Older Adults
