# VoxCeleb Enrichment for Age and Gender Recognition

arXiv: https://arxiv.org/abs/2109.13510 · HTML: https://mlsanity.com/paper/2109.13510/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 28 Sep 2021.

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

- [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)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars](https://mlsanity.com/paper/2008.10174/) (2020)
- [Towards Learning a Universal Non-Semantic Representation of Speech](https://mlsanity.com/paper/2002.12764/) (2020)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [AutoSpeech: Neural Architecture Search for Speaker Recognition](https://mlsanity.com/paper/2005.03215/) (2020)
- [I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences](https://mlsanity.com/paper/1904.07386/) (2019)

## Built on by (most cited first, 20 of 20)

- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://mlsanity.com/paper/2303.00733/) (2023)
- [AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs](https://mlsanity.com/paper/2510.07293/) (2025)
- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [WavReward: Spoken Dialogue Models With Generalist Reward Evaluators](https://mlsanity.com/paper/2505.09558/) (2025)
- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox](https://mlsanity.com/paper/2605.27772/) (2026)
- [WavBench: Benchmarking Reasoning, Colloquialism, and Paralinguistics for End-to-End Spoken Dialogue Models](https://mlsanity.com/paper/2602.12135/) (2026)
- [Demographic Attributes Prediction from Speech Using WavLM Embeddings](https://mlsanity.com/paper/2502.12007/) (2025)
- [PAL: Probing Audio Encoders via LLMs -- Audio Information Transfer into LLMs](https://mlsanity.com/paper/2506.10423/) (2025)
- [Enhancing Dialogue Annotation with Speaker Characteristics Leveraging a Frozen LLM](https://mlsanity.com/paper/2508.04795/) (2025)
- [Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings](https://mlsanity.com/paper/2209.12602/) (2022)
- [Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models](https://mlsanity.com/paper/2510.01157/) (2025)
- [Cross-Language Speaker Attribute Prediction Using MIL and RL](https://mlsanity.com/paper/2601.04257/) (2026)
- [Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation](https://mlsanity.com/paper/2603.10827/) (2026)
- [Greater accessibility can amplify discrimination in generative AI](https://mlsanity.com/paper/2603.22260/) (2026)
- [Leveraging Speaker Embeddings with Adversarial Multi-task Learning for Age Group Classification](https://mlsanity.com/paper/2301.09058/) (2023)
- [Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification](https://mlsanity.com/paper/2409.15974/) (2024)
- [VANPY: Voice Analysis Framework](https://mlsanity.com/paper/2502.17579/) (2025)
- [Voice Privacy from an Attribute-based Perspective](https://mlsanity.com/paper/2603.20301/) (2026)

## Abstract (arXiv)

VoxCeleb datasets are widely used in speaker recognition studies. Our work serves two purposes. First, we provide speaker age labels and (an alternative) annotation of speaker gender. Second, we demonstrate the use of this metadata by constructing age and gender recognition models with different features and classifiers. We query different celebrity databases and apply consensus rules to derive age and gender labels. We also compare the original VoxCeleb gender labels with our labels to identify records that might be mislabeled in the original VoxCeleb data. On modeling side, we design a comprehensive study of multiple features and models for recognizing gender and age. Our best system, using i-vector features, achieved an F1-score of 0.9829 for gender recognition task using logistic regression, and the lowest mean absolute error (MAE) in age regression, 9.443 years, is obtained with ridge regression. This indicates challenge in age estimation from in-the-wild style speech data.