# Towards Speaker Age Estimation with Label Distribution Learning

arXiv: https://arxiv.org/abs/2202.11424 · HTML: https://mlsanity.com/paper/2202.11424/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [Speech2Video: Cross-Modal Distillation for Speech to Video Generation](https://mlsanity.com/paper/2107.04806/) (2021)

## Built on by (most cited first, 13 of 13)

- [Inaccurate Label Distribution Learning](https://mlsanity.com/paper/2302.13000/) (2023)
- [Label Distribution Learning via Implicit Distribution Representation](https://mlsanity.com/paper/2209.13824/) (2022)
- [SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning](https://mlsanity.com/paper/2206.13101/) (2022)
- [Uncertainty Calibration for Deep Audio Classifiers](https://mlsanity.com/paper/2206.13071/) (2022)
- [Gait-based Age Group Classification with Adaptive Graph Neural Network](https://mlsanity.com/paper/2210.00294/) (2022)
- [SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning](https://mlsanity.com/paper/2210.09524/) (2022)
- [Boosting Star-GANs for Voice Conversion with Contrastive Discriminator](https://mlsanity.com/paper/2209.10088/) (2022)
- [Label distribution learning via label correlation grid](https://mlsanity.com/paper/2210.08184/) (2022)
- [TabMixer: Excavating Label Distribution Learning with Small-scale Features](https://mlsanity.com/paper/2210.13852/) (2022)
- [Leveraging Speaker Embeddings with Adversarial Multi-task Learning for Age Group Classification](https://mlsanity.com/paper/2301.09058/) (2023)
- [Data Augmentation For Label Enhancement](https://mlsanity.com/paper/2303.11698/) (2023)
- [Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification](https://mlsanity.com/paper/2312.06522/) (2023)
- [Latent label distribution grid representation for modeling uncertainty](https://mlsanity.com/paper/2505.21180/) (2025)

## Abstract (arXiv)

Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise age identification remains a challenge due to label ambiguity, i.e., utterances from adjacent age of the same person are often indistinguishable. To address this, we utilize the ambiguous information among the age labels, convert each age label into a discrete label distribution and leverage the label distribution learning (LDL) method to fit the data. For each audio data sample, our method produces a age distribution of its speaker, and on top of the distribution we also perform two other tasks: age prediction and age uncertainty minimization. Therefore, our method naturally combines the age classification and regression approaches, which enhances the robustness of our method. We conduct experiments on the public NIST SRE08-10 dataset and a real-world dataset, which exhibit that our method outperforms baseline methods by a relatively large margin, yielding a 10% reduction in terms of mean absolute error (MAE) on a real-world dataset.