# Data Augmentation For Children's Speech Recognition -- The "Ethiopian" System For The SLT 2021 Children Speech Recognition Challenge

arXiv: https://arxiv.org/abs/2011.04547 · HTML: https://mlsanity.com/paper/2011.04547/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

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

- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System](https://mlsanity.com/paper/2106.09963/) (2021)
- [Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition](https://mlsanity.com/paper/2506.07515/) (2025)
- [Automatic Speech Recognition for Speech Assessment of Persian Preschool Children](https://mlsanity.com/paper/2203.12886/) (2022)
- [Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project](https://mlsanity.com/paper/2309.15869/) (2023)
- [Regularizing Learnable Feature Extraction for Automatic Speech Recognition](https://mlsanity.com/paper/2506.09804/) (2025)
- [Automated evaluation of children's speech fluency for low-resource languages](https://mlsanity.com/paper/2505.19671/) (2025)
- [Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR](https://mlsanity.com/paper/2403.10937/) (2024)

## Abstract (arXiv)

This paper presents the "Ethiopian" system for the SLT 2021 Children Speech Recognition Challenge. Various data processing and augmentation techniques are proposed to tackle children's speech recognition problem, especially the lack of the children's speech recognition training data issue. Detailed experiments are designed and conducted to show the effectiveness of each technique, across different speech recognition toolkits and model architectures. Step by step, we explain how we come up with our final system, which provides the state-of-the-art results in the SLT 2021 Children Speech Recognition Challenge, with 21.66% CER on the Track 1 evaluation set (4th place overall), and 16.53% CER on the Track 2 evaluation set (1st place overall). Post-challenge analysis shows that our system actually achieves 18.82% CER on the Track 1 evaluation set, but we submitted the wrong version to the challenge organizer for Track 1.