# Speech-based Age and Gender Prediction with Transformers

arXiv: https://arxiv.org/abs/2306.16962 · HTML: https://mlsanity.com/paper/2306.16962/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2023.

## Built on by (most cited first, 14 of 14)

- [Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens](https://mlsanity.com/paper/2503.01710/) (2025)
- [Scaling Rich Style-Prompted Text-to-Speech Datasets](https://mlsanity.com/paper/2503.04713/) (2025)
- [CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech](https://mlsanity.com/paper/2506.02863/) (2025)
- [VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models](https://mlsanity.com/paper/2601.19956/) (2026)
- [MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks](https://mlsanity.com/paper/2507.23511/) (2025)
- [VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model](https://mlsanity.com/paper/2509.21108/) (2025)
- [Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach](https://mlsanity.com/paper/2505.14449/) (2025)
- [Gender Bias in Instruction-Guided Speech Synthesis Models](https://mlsanity.com/paper/2502.05649/) (2025)
- [Audio-based Kinship Verification Using Age Domain Conversion](https://mlsanity.com/paper/2410.11120/) (2024)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization](https://mlsanity.com/paper/2512.14687/) (2025)
- [Exploring Robust Face-Voice Matching in Multilingual Environments](https://mlsanity.com/paper/2407.19875/) (2024)
- [VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling](https://mlsanity.com/paper/2603.01270/) (2026)
- [VANPY: Voice Analysis Framework](https://mlsanity.com/paper/2502.17579/) (2025)

## Abstract (arXiv)

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.