# Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model

arXiv: https://arxiv.org/abs/2203.11774 · HTML: https://mlsanity.com/paper/2203.11774/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Joint gender and age estimation based on speech signals using x-vectors and transfer learning](https://mlsanity.com/paper/2012.01551/) (2020)
- [End-to-End Speaker Height and age estimation using Attention Mechanism with LSTM-RNN](https://mlsanity.com/paper/2101.05056/) (2021)
- [Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling](https://mlsanity.com/paper/2110.13653/) (2021)

## Built on by (most cited first, 7 of 7)

- [Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection](https://mlsanity.com/paper/2406.17376/) (2024)
- [Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification](https://mlsanity.com/paper/2404.18739/) (2024)
- [Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification](https://mlsanity.com/paper/2309.14838/) (2023)
- [SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning](https://mlsanity.com/paper/2210.09524/) (2022)
- [Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models](https://mlsanity.com/paper/2506.14153/) (2025)
- [Tessellated Linear Model for Age Prediction from Voice](https://mlsanity.com/paper/2501.09229/) (2025)
- [Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment](https://mlsanity.com/paper/2506.14148/) (2025)

## Abstract (arXiv)

The estimation of speaker characteristics such as age and height is a challenging task, having numerous applications in voice forensic analysis. In this work, we propose a bi-encoder transformer mixture model for speaker age and height estimation. Considering the wide differences in male and female voice characteristics such as differences in formant and fundamental frequencies, we propose the use of two separate transformer encoders for the extraction of specific voice features in the male and female gender, using wav2vec 2.0 as a common-level feature extractor. This architecture reduces the interference effects during backpropagation and improves the generalizability of the model. We perform our experiments on the TIMIT dataset and significantly outperform the current state-of-the-art results on age estimation. Specifically, we achieve root mean squared error (RMSE) of 5.54 years and 6.49 years for male and female age estimation, respectively. Further experiment to evaluate the relative importance of different phonetic types for our task demonstrate that vowel sounds are the most distinguishing for age estimation.