mlsanityData to 7 Oct 2026

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

arXiv 1910.12094 ↗First posted 26 Oct 2019cs.SD cs.CL eess.AScited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2019.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 5; 2024: 3; 2025: 4; 2026: 1.

29 papers.

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  1. 2021Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyondcited by 151
  2. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
  3. 2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
  4. 2021Exploiting Adapters for Cross-lingual Low-resource Speech Recognitioncited by 33
  5. 2023MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognitioncited by 31
  6. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  7. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  8. 2021Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognitioncited by 16
  9. 2021On sensitivity of meta-learning to support datacited by 15
  10. 2023Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learningcited by 9
  11. 2022Improved Meta Learning for Low Resource Speech Recognitioncited by 7
  12. 2022Text-To-Speech Data Augmentation for Low Resource Speech Recognitioncited by 6
17 more
  1. 2025Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translationcited by 6
  2. 2024SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognitioncited by 4
  3. 2021Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varietiescited by 2
  4. 2021Rapid Model Architecture Adaption for Meta-Learningcited by 2
  5. 2024Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Reviewcited by 2
  6. 2021Challenges and Opportunities of Speech Recognition for Bengali Languagecited by 1
  7. 2022Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognitioncited by 1
  8. 2023Multilingual Sentence-Level Semantic Search using Meta-Distillation Learningcited by 1
  9. 2023PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognitioncited by 1
  10. 2025SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptationcited by 1
  11. 2022Adaptive Activation Network For Low Resource Multilingual Speech Recognitioncited by 0
  12. 2022Meta Auxiliary Learning for Low-resource Spoken Language Understandingcited by 0
  13. 2023A meta learning scheme for fast accent domain expansion in Mandarin speech recognitioncited by 0
  14. 2024Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learningcited by 0
  15. 2025Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Datacited by 0
  16. 2025Heterogeneous Self-Supervised Acoustic Pre-Training with Local Constraintscited by 0
  17. 2026Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluationcited by 0