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In this paper we extend the x-vector framework for the task of speaker's ageestimation and gender classification. In particular, we replace the baselinemultilayer-TDNN architecture with QuartzNet, a convolutional architecture thathas gained success in the field of speech recognition. We further propose atwo-staged transfer learning scheme, utilizing large scale speech datasets:VoxCeleb and Common Voice, and usage of multitask learning to allow for jointage estimation and gender classification with a single system. We train andevaluate the performance on the TIMIT dataset. The proposed transfer learningscheme yields consecutive performance improvements in terms of both ageestimation error and gender classification accuracy and the best performingsystem achieves new state-of-the-art results on the task of age estimation onthe TIMIT TEST dataset with MAE of 5.12 and 5.29 years and RMSE of 7.24 and8.12 years for male and female speakers respectively while maintaining a genderclassification accuracy of 99.6%.

Joint gender and age estimation based on speech signals using x-vectors and transfer learning