# ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5

arXiv: https://arxiv.org/abs/2409.18584 · HTML: https://mlsanity.com/paper/2409.18584/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2024.

## Built on by (most cited first, 8 of 8)

- [InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue](https://mlsanity.com/paper/2510.13747/) (2025)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [TELEVAL: A Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios](https://mlsanity.com/paper/2507.18061/) (2025)
- [Abjad-Kids: An Arabic Speech Classification Dataset for Primary Education](https://mlsanity.com/paper/2603.20255/) (2026)
- [When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools](https://mlsanity.com/paper/2603.24389/) (2026)
- [Back to Basics: Revisiting ASR in the Age of Voice Agents](https://mlsanity.com/paper/2603.25727/) (2026)
- [MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning](https://mlsanity.com/paper/2607.27109/) (2026)
- [AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech](https://mlsanity.com/paper/2610.05044/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0 and HuBERT. However, developing robust ASR models for young children's speech remains challenging due to differences in pronunciation, tone, and pace compared to adult speech. In this paper, we introduce a new Mandarin speech dataset focused on children aged 3 to 5, addressing the scarcity of resources in this area. The dataset comprises 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. We provide a comprehensive analysis of speaker demographics, speech duration distribution and geographic coverage. Additionally, we evaluate ASR performance on models trained from scratch, such as Conformer, as well as fine-tuned pre-trained models like HuBERT and Whisper, where fine-tuning demonstrates significant performance improvements. Furthermore, we assess speaker verification (SV) on our dataset, showing that, despite the challenges posed by the unique vocal characteristics of young children, the dataset effectively supports both ASR and SV tasks. This dataset is a valuable contribution to Mandarin child speech research. The dataset is now open-source and freely available for all academic purposes on https://github.com/flageval-baai/ChildMandarin.