# SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning

arXiv: https://arxiv.org/abs/2210.09524 · HTML: https://mlsanity.com/paper/2210.09524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression](https://mlsanity.com/paper/2204.00309/) (2022)
- [Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition](https://mlsanity.com/paper/2003.10724/) (2020)
- [Towards Speaker Age Estimation with Label Distribution Learning](https://mlsanity.com/paper/2202.11424/) (2022)
- [Speech2Video: Cross-Modal Distillation for Speech to Video Generation](https://mlsanity.com/paper/2107.04806/) (2021)
- [Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model](https://mlsanity.com/paper/2203.11774/) (2022)
- [SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning](https://mlsanity.com/paper/2206.13101/) (2022)
- [Phone Duration Modeling for Speaker Age Estimation in Children](https://mlsanity.com/paper/2109.01568/) (2021)

## Built on by (most cited first, 2 of 2)

- [Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech](https://mlsanity.com/paper/2508.10332/) (2025)
- [How Well Do Self-Supervised Speech Models Encode Age and Gender in Children's Speech? A Layer-Wise Analysis Across Multiple Architectures](https://mlsanity.com/paper/2606.22177/) (2026)

## Abstract (arXiv)

Estimating age from a single speech is a classic and challenging topic. Although Label Distribution Learning (LDL) can represent adjacent indistinguishable ages well, the uncertainty of the age estimate for each utterance varies from person to person, i.e., the variance of the age distribution is different. To address this issue, we propose selective variance label distribution learning (SVLDL) method to adapt the variance of different age distributions. Furthermore, the model uses WavLM as the speech feature extractor and adds the auxiliary task of gender recognition to further improve the performance. Two tricks are applied on the loss function to enhance the robustness of the age estimation and improve the quality of the fitted age distribution. Extensive experiments show that the model achieves state-of-the-art performance on all aspects of the NIST SRE08-10 and a real-world datasets.