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

arXiv: https://arxiv.org/abs/2012.01551 · HTML: https://mlsanity.com/paper/2012.01551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model](https://mlsanity.com/paper/2203.11774/) (2022)
- [Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling](https://mlsanity.com/paper/2110.13653/) (2021)
- [Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction](https://mlsanity.com/paper/2206.12568/) (2022)
- [TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches](https://mlsanity.com/paper/2404.12077/) (2024)
- [Tessellated Linear Model for Age Prediction from Voice](https://mlsanity.com/paper/2501.09229/) (2025)

## Abstract (arXiv)

In this paper we extend the x-vector framework for the task of speaker's age estimation and gender classification. In particular, we replace the baseline multilayer-TDNN architecture with QuartzNet, a convolutional architecture that has gained success in the field of speech recognition. We further propose a two-staged transfer learning scheme, utilizing large scale speech datasets: VoxCeleb and Common Voice, and usage of multitask learning to allow for joint age estimation and gender classification with a single system. We train and evaluate the performance on the TIMIT dataset. The proposed transfer learning scheme yields consecutive performance improvements in terms of both age estimation error and gender classification accuracy and the best performing system achieves new state-of-the-art results on the task of age estimation on the TIMIT TEST dataset with MAE of 5.12 and 5.29 years and RMSE of 7.24 and 8.12 years for male and female speakers respectively while maintaining a gender classification accuracy of 99.6%.