Audio-based Kinship Verification Using Age Domain Conversion

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 insights for future kinship verification research.