# Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling

arXiv: https://arxiv.org/abs/2110.13653 · HTML: https://mlsanity.com/paper/2110.13653/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 24 Oct 2021.

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

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Learning Robust and Multilingual Speech Representations](https://mlsanity.com/paper/2001.11128/) (2020)
- [diffGrad: An Optimization Method for Convolutional Neural Networks](https://mlsanity.com/paper/1909.11015/) (2019)
- [Learning Speaker Representations with Mutual Information](https://mlsanity.com/paper/1812.00271/) (2018)
- [Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks](https://mlsanity.com/paper/1910.10387/) (2019)
- [NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling](https://mlsanity.com/paper/2007.06021/) (2020)
- [Joint gender and age estimation based on speech signals using x-vectors and transfer learning](https://mlsanity.com/paper/2012.01551/) (2020)

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

- [Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model](https://mlsanity.com/paper/2203.11774/) (2022)
- [Tessellated Linear Model for Age Prediction from Voice](https://mlsanity.com/paper/2501.09229/) (2025)

## Abstract (arXiv)

Speaker profiling, which aims to estimate speaker characteristics such as age and height, has a wide range of applications inforensics, recommendation systems, etc. In this work, we propose a semisupervised learning approach to mitigate the issue of low training data for speaker profiling. This is done by utilizing external corpus with speaker information to train a better representation which can help to improve the speaker profiling systems. Specifically, besides the standard supervised learning path, the proposed framework has two more paths: (1) an unsupervised speaker representation learning path that helps to capture the speaker information; (2) a consistency training path that helps to improve the robustness of the system by enforcing it to produce similar predictions for utterances of the same speaker.The proposed approach is evaluated on the TIMIT and NISP datasets for age, height, and gender estimation, while the Librispeech is used as the unsupervised external corpus. Trained both on single-task and multi-task settings, our approach was able to achieve state-of-the-art results on age estimation on the TIMIT Test dataset with Root Mean Square Error(RMSE) of6.8 and 7.4 years and Mean Absolute Error(MAE) of 4.8 and5.0 years for male and female speakers respectively.