  VoxCeleb datasets are widely used in speaker recognition studies. Our work
serves two purposes. First, we provide speaker age labels and (an alternative)
annotation of speaker gender. Second, we demonstrate the use of this metadata
by constructing age and gender recognition models with different features and
classifiers. We query different celebrity databases and apply consensus rules
to derive age and gender labels. We also compare the original VoxCeleb gender
labels with our labels to identify records that might be mislabeled in the
original VoxCeleb data. On modeling side, we design a comprehensive study of
multiple features and models for recognizing gender and age. Our best system,
using i-vector features, achieved an F1-score of 0.9829 for gender recognition
task using logistic regression, and the lowest mean absolute error (MAE) in age
regression, 9.443 years, is obtained with ridge regression. This indicates
challenge in age estimation from in-the-wild style speech data.
