# Voice Aging with Audio-Visual Style Transfer

arXiv: https://arxiv.org/abs/2110.02411 · HTML: https://mlsanity.com/paper/2110.02411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Seeing Voices and Hearing Faces: Cross-modal biometric matching](https://mlsanity.com/paper/1804.00326/) (2018)
- [Learnable PINs: Cross-Modal Embeddings for Person Identity](https://mlsanity.com/paper/1805.00833/) (2018)
- [Face Aging With Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1702.01983/) (2017)
- [Neural Style Transfer for Audio Spectograms](https://mlsanity.com/paper/1801.01589/) (2018)
- [Voice Impersonation using Generative Adversarial Networks](https://mlsanity.com/paper/1802.06840/) (2018)
- [Audio style transfer](https://mlsanity.com/paper/1710.11385/) (2017)
- [High-quality nonparallel voice conversion based on cycle-consistent adversarial network](https://mlsanity.com/paper/1804.00425/) (2018)
- [MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms](https://mlsanity.com/paper/1910.03713/) (2019)

## Abstract (arXiv)

Face aging techniques have used generative adversarial networks (GANs) and style transfer learning to transform one's appearance to look younger/older. Identity is maintained by conditioning these generative networks on a learned vector representation of the source content. In this work, we apply a similar approach to age a speaker's voice, referred to as voice aging. We first analyze the classification of a speaker's age by training a convolutional neural network (CNN) on the speaker's voice and face data from Common Voice and VoxCeleb datasets. We generate aged voices from style transfer to transform an input spectrogram to various ages and demonstrate our method on a mobile app.