# Learning Fast Adaptation on Cross-Accented Speech Recognition

arXiv: https://arxiv.org/abs/2003.01901 · HTML: https://mlsanity.com/paper/2003.01901/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://mlsanity.com/paper/2303.13592/) (2023)
- [Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2105.03953/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Towards Lifelong Learning of End-to-end ASR](https://mlsanity.com/paper/2104.01616/) (2021)
- [Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review](https://mlsanity.com/paper/2211.09511/) (2022)
- [Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset](https://mlsanity.com/paper/2201.02419/) (2022)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [Supervised Contrastive Learning for Accented Speech Recognition](https://mlsanity.com/paper/2107.00921/) (2021)
- [MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks](https://mlsanity.com/paper/2102.04199/) (2021)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [Greenformers: Improving Computation and Memory Efficiency in Transformer Models via Low-Rank Approximation](https://mlsanity.com/paper/2108.10808/) (2021)
- [X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing](https://mlsanity.com/paper/2106.03777/) (2021)
- [DENOASR: Debiasing ASRs through Selective Denoising](https://mlsanity.com/paper/2410.16712/) (2024)
- [Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning](https://mlsanity.com/paper/2305.13080/) (2023)
- [Writer adaptation for offline text recognition: An exploration of neural network-based methods](https://mlsanity.com/paper/2307.15071/) (2023)
- [Accented Speech Recognition Inspired by Human Perception](https://mlsanity.com/paper/2104.04627/) (2021)
- [Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition](https://mlsanity.com/paper/2202.12326/) (2022)
- [Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition](https://mlsanity.com/paper/2306.14517/) (2023)
- [PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition](https://mlsanity.com/paper/2312.08571/) (2023)
- [Improving Self-supervised Pre-training using Accent-Specific Codebooks](https://mlsanity.com/paper/2407.03734/) (2024)
- [Mixture-of-Experts with Intermediate CTC Supervision for Accented Speech Recognition](https://mlsanity.com/paper/2602.01967/) (2026)
- [Taming the Beast: Learning to Control Neural Conversational Models](https://mlsanity.com/paper/2108.10561/) (2021)
- [A meta learning scheme for fast accent domain expansion in Mandarin speech recognition](https://mlsanity.com/paper/2307.12262/) (2023)
- [Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition](https://mlsanity.com/paper/2509.02514/) (2025)
- [Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy](https://mlsanity.com/paper/2604.01705/) (2026)

## Abstract (arXiv)

Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system. In this paper, we introduce a cross-accented English speech recognition task as a benchmark for measuring the ability of the model to adapt to unseen accents using the existing CommonVoice corpus. We also propose an accent-agnostic approach that extends the model-agnostic meta-learning (MAML) algorithm for fast adaptation to unseen accents. Our approach significantly outperforms joint training in both zero-shot, few-shot, and all-shot in the mixed-region and cross-region settings in terms of word error rate.