  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.
