# Audio-based Kinship Verification Using Age Domain Conversion

arXiv: https://arxiv.org/abs/2410.11120 · HTML: https://mlsanity.com/paper/2410.11120/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era](https://mlsanity.com/paper/2210.03538/) (2022)
- [CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion](https://mlsanity.com/paper/2010.11672/) (2020)
- [Speech-based Age and Gender Prediction with Transformers](https://mlsanity.com/paper/2306.16962/) (2023)
- [Deep Fusion Siamese Network for Automatic Kinship Verification](https://mlsanity.com/paper/2006.00143/) (2020)
- [An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation](https://mlsanity.com/paper/2107.08361/) (2021)
- [Data Augmentation for Dementia Detection in Spoken Language](https://mlsanity.com/paper/2206.12879/) (2022)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion](https://mlsanity.com/paper/2203.15873/) (2022)
- [Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification](https://mlsanity.com/paper/2309.16369/) (2023)
- [Hear Your Face: Face-based voice conversion with F0 estimation](https://mlsanity.com/paper/2408.09802/) (2024)
- [Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion](https://mlsanity.com/paper/2311.08104/) (2023)

## Built on by (most cited first, 3 of 3)

- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment](https://mlsanity.com/paper/2412.15114/) (2024)
- [A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Dataset](https://mlsanity.com/paper/2609.14145/) (2026)

## Abstract (arXiv)

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.