Computer Science > Sound
arXiv:2306.16962 (cs)
[Submitted on 29 Jun 2023]
Title:Speech-based Age and Gender Prediction with Transformers
View a PDF of the paper titled Speech-based Age and Gender Prediction with Transformers, by Felix Burkhardt and 4 other authors
View PDFAbstract: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.
| Comments: | 5 pages, submitted to 15th ITG Conference on Speech Communication |
| Subjects: | Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2306.16962 [cs.SD] |
| (orarXiv:2306.16962v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2306.16962 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Speech-based Age and Gender Prediction with Transformers, by Felix Burkhardt and 4 other authors
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