Research paper · 2023-06-29
Speech-based Age and Gender Prediction with Transformers
Felix Burkhardt, Johannes Wagner, Hagen Wierstorf, Florian Eyben, Björn Schuller
Published2023-06-29
Authors5
Citing Models2
arXiv2306.16962
Abstract
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.
Details
- arXiv identifier
- 2306.16962
- Published
- 2023-06-29
- Authors
- Felix Burkhardt, Johannes Wagner, Hagen Wierstorf, Florian Eyben, Björn Schuller
Models That Cite This Paper
- Described bywav2vec2-large-robust-24-ft-age-gender
- Described bywav2vec2-large-robust-6-ft-age-gender