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Speech-based Age and Gender Prediction with Transformers
| Authors | Burkhardt Felix, Wagner Johannes, Wierstorf Hagen, Eyben Florian, Schuller Björn |
| Title | Speech-based Age and Gender Prediction with Transformers |
| 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 hand-crafted 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. |
| Conference | Speech Communication; 15th ITG Conference |
| Date | 20-22 September 2023 |
| Location | Aachen |
| Year of Publication | 2023 |
| Publisher | VDE |
| Url | https://doi.org/10.5281/zenodo.10664760 |
| DOI | 10.30420/456164008 |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
