Speech-based Age and Gender Prediction with Transformers
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 approach built on handcrafted features, our proposed system shows an improvement of 9 reproducible, we release the best performing model to the community as well as the sample lists of the data splits.
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