# Non-native children speech recognition through transfer learning

arXiv: https://arxiv.org/abs/1809.09658 · HTML: https://mlsanity.com/paper/1809.09658/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning](https://mlsanity.com/paper/2110.00678/) (2021)
- [Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition](https://mlsanity.com/paper/2505.20006/) (2025)
- [Mixtures of Deep Neural Experts for Automated Speech Scoring](https://mlsanity.com/paper/2106.12475/) (2021)
- [Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding](https://mlsanity.com/paper/2202.05209/) (2022)
- [Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation](https://mlsanity.com/paper/2102.11488/) (2021)
- [Social Fraud Detection Review: Methods, Challenges and Analysis](https://mlsanity.com/paper/2111.05645/) (2021)
- [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2606.21340/) (2026)

## Abstract (arXiv)

This work deals with non-native children's speech and investigates both multi-task and transfer learning approaches to adapt a multi-language Deep Neural Network (DNN) to speakers, specifically children, learning a foreign language. The application scenario is characterized by young students learning English and German and reading sentences in these second-languages, as well as in their mother language. The paper analyzes and discusses techniques for training effective DNN-based acoustic models starting from children native speech and performing adaptation with limited non-native audio material. A multi-lingual model is adopted as baseline, where a common phonetic lexicon, defined in terms of the units of the International Phonetic Alphabet (IPA), is shared across the three languages at hand (Italian, German and English); DNN adaptation methods based on transfer learning are evaluated on significant non-native evaluation sets. Results show that the resulting non-native models allow a significant improvement with respect to a mono-lingual system adapted to speakers of the target language.