# Tessellated Linear Model for Age Prediction from Voice

arXiv: https://arxiv.org/abs/2501.09229 · HTML: https://mlsanity.com/paper/2501.09229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Optimal Change-Point Detection and Localization](https://mlsanity.com/paper/2010.11470/) (2020)
- [Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model](https://mlsanity.com/paper/2203.11774/) (2022)
- [Fast Linear Model Trees by PILOT](https://mlsanity.com/paper/2302.03931/) (2023)
- [Joint gender and age estimation based on speech signals using x-vectors and transfer learning](https://mlsanity.com/paper/2012.01551/) (2020)
- [Neural Regression Trees](https://mlsanity.com/paper/1810.00974/) (2018)
- [Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling](https://mlsanity.com/paper/2110.13653/) (2021)
- [TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches](https://mlsanity.com/paper/2404.12077/) (2024)

## Built on by (most cited first, 1 of 1)

- [VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling](https://mlsanity.com/paper/2603.01270/) (2026)

## Abstract (arXiv)

Voice biometric tasks, such as age estimation require modeling the often complex relationship between voice features and the biometric variable. While deep learning models can handle such complexity, they typically require large amounts of accurately labeled data to perform well. Such data are often scarce for biometric tasks such as voice-based age prediction. On the other hand, simpler models like linear regression can work with smaller datasets but often fail to generalize to the underlying non-linear patterns present in the data. In this paper we propose the Tessellated Linear Model (TLM), a piecewise linear approach that combines the simplicity of linear models with the capacity of non-linear functions. TLM tessellates the feature space into convex regions and fits a linear model within each region. We optimize the tessellation and the linear models using a hierarchical greedy partitioning. We evaluated TLM on the TIMIT dataset on the task of age prediction from voice, where it outperformed state-of-the-art deep learning models.