# Meta Learning for End-to-End Low-Resource Speech Recognition

arXiv: https://arxiv.org/abs/1910.12094 · HTML: https://mlsanity.com/paper/1910.12094/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 29 of 29)

- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation](https://mlsanity.com/paper/2106.09017/) (2021)
- [Exploiting Adapters for Cross-lingual Low-resource Speech Recognition](https://mlsanity.com/paper/2105.11905/) (2021)
- [MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition](https://mlsanity.com/paper/2302.13750/) (2023)
- [End-to-End Speech Recognition from Federated Acoustic Models](https://mlsanity.com/paper/2104.14297/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition](https://mlsanity.com/paper/2109.09161/) (2021)
- [On sensitivity of meta-learning to support data](https://mlsanity.com/paper/2110.13953/) (2021)
- [Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning](https://mlsanity.com/paper/2306.15686/) (2023)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Text-To-Speech Data Augmentation for Low Resource Speech Recognition](https://mlsanity.com/paper/2204.00291/) (2022)
- [Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation](https://mlsanity.com/paper/2502.17380/) (2025)
- [SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition](https://mlsanity.com/paper/2409.10429/) (2024)
- [Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties](https://mlsanity.com/paper/2104.01624/) (2021)
- [Rapid Model Architecture Adaption for Meta-Learning](https://mlsanity.com/paper/2109.04925/) (2021)
- [Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review](https://mlsanity.com/paper/2408.10330/) (2024)
- [Challenges and Opportunities of Speech Recognition for Bengali Language](https://mlsanity.com/paper/2109.13217/) (2021)
- [Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition](https://mlsanity.com/paper/2202.12326/) (2022)
- [Multilingual Sentence-Level Semantic Search using Meta-Distillation Learning](https://mlsanity.com/paper/2309.08185/) (2023)
- [PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition](https://mlsanity.com/paper/2312.08571/) (2023)
- [SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation](https://mlsanity.com/paper/2512.21204/) (2025)
- [Adaptive Activation Network For Low Resource Multilingual Speech Recognition](https://mlsanity.com/paper/2205.14326/) (2022)
- [Meta Auxiliary Learning for Low-resource Spoken Language Understanding](https://mlsanity.com/paper/2206.12774/) (2022)
- [A meta learning scheme for fast accent domain expansion in Mandarin speech recognition](https://mlsanity.com/paper/2307.12262/) (2023)
- [Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning](https://mlsanity.com/paper/2412.01408/) (2024)
- [Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data](https://mlsanity.com/paper/2503.22749/) (2025)
- [Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraints](https://mlsanity.com/paper/2508.19990/) (2025)
- [Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation](https://mlsanity.com/paper/2606.17820/) (2026)

## Abstract (arXiv)

In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to achieve fast adaptation on unseen target language, via recently proposed model-agnostic meta learning algorithm (MAML). We evaluated the proposed approach using six languages as pretraining tasks and four languages as target tasks. Preliminary results showed that the proposed method, MetaASR, significantly outperforms the state-of-the-art multitask pretraining approach on all target languages with different combinations of pretraining languages. In addition, since MAML's model-agnostic property, this paper also opens new research direction of applying meta learning to more speech-related applications.