We report on the curation of several publicly available datasets for age and gender prediction. Furthermore, we present experiments to predict age and gender with models based on a pre-trained wav2vec 2.0. Depending on the dataset, we achieve an MAE between 7.1 years and 10.8 years for age, and at least 91.1% ACC for gender (female, male, child). Compared to a modelling approach built on handcrafted features, our proposed system shows an improvement of 9% UAR for age and 4% UAR for gender. To make our findings reproducible, we release the best performing model to the community as well as the sample lists of the data splits.
Cite
@article{arxiv.2306.16962,
title = {Speech-based Age and Gender Prediction with Transformers},
author = {Felix Burkhardt and Johannes Wagner and Hagen Wierstorf and Florian Eyben and Björn Schuller},
journal= {arXiv preprint arXiv:2306.16962},
year = {2023}
}
Comments
5 pages, submitted to 15th ITG Conference on Speech Communication