# Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations

arXiv: https://arxiv.org/abs/2211.07769 · HTML: https://mlsanity.com/paper/2211.07769/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Vector-Quantized Autoregressive Predictive Coding](https://mlsanity.com/paper/2005.08392/) (2020)
- [Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition](https://mlsanity.com/paper/1912.01679/) (2019)
- [Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation](https://mlsanity.com/paper/2005.08575/) (2020)
- [End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study](https://mlsanity.com/paper/2102.09918/) (2021)
- [Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks](https://mlsanity.com/paper/1910.10387/) (2019)
- [Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning](https://mlsanity.com/paper/2109.07349/) (2021)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning](https://mlsanity.com/paper/2110.00678/) (2021)
- [Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR](https://mlsanity.com/paper/2102.06816/) (2021)

## Built on by (most cited first, 3 of 3)

- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [Error-preserving Automatic Speech Recognition of Young English Learners' Language](https://mlsanity.com/paper/2406.03235/) (2024)
- [Child ASR Adaptation with Adult Retention: An Empirical Study](https://mlsanity.com/paper/2610.08827/) (2026)

## Abstract (arXiv)

Children's speech recognition is a vital, yet largely overlooked domain when building inclusive speech technologies. The major challenge impeding progress in this domain is the lack of adequate child speech corpora; however, recent advances in self-supervised learning have created a new opportunity for overcoming this problem of data scarcity. In this paper, we leverage self-supervised adult speech representations and use three well-known child speech corpora to build models for children's speech recognition. We assess the performance of fine-tuning on both native and non-native children's speech, examine the effect of cross-domain child corpora, and investigate the minimum amount of child speech required to fine-tune a model which outperforms a state-of-the-art adult model. We also analyze speech recognition performance across children's ages. Our results demonstrate that fine-tuning with cross-domain child corpora leads to relative improvements of up to 46.08% and 45.53% for native and non-native child speech respectively, and absolute improvements of 14.70% and 31.10%. We also show that with as little as 5 hours of transcribed children's speech, it is possible to fine-tune a children's speech recognition system that outperforms a state-of-the-art adult model fine-tuned on 960 hours of adult speech.