mlsanityData to 14 Oct 2026

Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning

arXiv 2110.00678 ↗First posted 1 Oct 2021eess.AS cs.CL cs.LGcited by 10

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Abstract (arXiv)

To address the performance gap of English ASR models on L2 English speakers, we evaluate fine-tuning of pretrained wav2vec 2.0 models (Baevski et al., 2020; Xu et al., 2021) on L2-ARCTIC, a non-native English speech corpus (Zhao et al., 2018) under different training settings. We compare (a) models trained with a combination of diverse accents to ones trained with only specific accents and (b) results from different single-accent models. Our experiments demonstrate the promise of developing ASR models for non-native English speakers, even with small amounts of L2 training data and even without a language model. Our models also excel in the zero-shot setting where we train on multiple L2 datasets and test on a blind L2 test set.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

19 of 38 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  4. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  5. 2015Listen, Attend and Spellcited by 543
  6. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
  7. 2015Attention-Based Models for Speech Recognitioncited by 401
  8. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 340
  9. 2019Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoderscited by 198
  10. 2019An Unsupervised Autoregressive Model for Speech Representation Learningcited by 186
  11. 2020Self-training and Pre-training are Complementary for Speech Recognitioncited by 99
  12. 2019Transformer-based Acoustic Modeling for Hybrid Speech Recognitioncited by 88
7 more
  1. 2019Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognitioncited by 66
  2. 2018Domain Adversarial Training for Accented Speech Recognitioncited by 40
  3. 2021The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methodscited by 24
  4. 2020REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabelingcited by 18
  5. 2021Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learningcited by 18
  6. 2018Non-native children speech recognition through transfer learningcited by 7
  7. 2019Independent language modeling architecture for end-to-end ASRcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

10 papers.

  1. 2021Adapting TTS models For New Speakers using Transfer Learningcited by 12
  2. 2023A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videoscited by 5
  3. 2025Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN Corpuscited by 5
  4. 2022Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decodingcited by 3
  5. 2022Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representationscited by 3
  6. 2023INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognitioncited by 3
  7. 2024LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognitioncited by 3
  8. 2025Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASRcited by 2
  9. 2023Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognitioncited by 1
  10. 2026Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalizationcited by 0