mlsanityData to 7 Oct 2026

SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition

arXiv 2409.10429 ↗First posted 16 Sep 2024eess.AS cs.CL cs.SDcited by 4

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Abstract (arXiv)

Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource languages but face significant challenges when applied to low-resource languages due to limited training data and insufficient cross-lingual generalization. Existing adaptation strategies, such as shallow fusion, data augmentation, and direct fine-tuning, either rely on external resources, suffer computational inefficiencies, or fail in test-time adaptation scenarios. To address these limitations, we introduce Speech Meta In-Context LEarning (SMILE), an innovative framework that combines meta-learning with speech in-context learning (SICL). SMILE leverages meta-training from high-resource languages to enable robust, few-shot generalization to low-resource languages without explicit fine-tuning on the target domain. Extensive experiments on the ML-SUPERB benchmark show that SMILE consistently outperforms baseline methods, significantly reducing character and word error rates in training-free few-shot multilingual ASR tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

27 of 37 references.

References · 27

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  4. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  5. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  6. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
  7. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
  8. 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 330
  9. 2023SeamlessM4T: Massively Multilingual & Multimodal Machine Translationcited by 124
  10. 2023ML-SUPERB: Multilingual Speech Universal PERformance Benchmarkcited by 70
  11. 2024mHuBERT-147: A Compact Multilingual HuBERT Modelcited by 64
  12. 2023Can Whisper perform speech-based in-context learning?cited by 35
15 more
  1. 2024LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASRcited by 35
  2. 2019Meta Learning for End-to-End Low-Resource Speech Recognitioncited by 29
  3. 2023Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuningcited by 29
  4. 2024It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognitioncited by 22
  5. 2023Can Contextual Biasing Remain Effective with Whisper and GPT-2?cited by 21
  6. 2022Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognitioncited by 19
  7. 2020Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognitioncited by 15
  8. 2023Massively Multilingual Shallow Fusion with Large Language Modelscited by 10
  9. 2024A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Setscited by 10
  10. 2022Improved Meta Learning for Low Resource Speech Recognitioncited by 7
  11. 2023A Novel Self-training Approach for Low-resource Speech Recognitioncited by 7
  12. 2021Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learningcited by 6
  13. 2024Empowering Low-Resource Language ASR via Large-Scale Pseudo Labelingcited by 4
  14. 2024Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Reviewcited by 2
  15. 2023Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognitioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languagescited by 3
  2. 2025Advancing STT for Low-Resource Real-World Speechcited by 1
  3. 2026MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learningcited by 1
  4. 2026In-Context Adaptation of Encoder-Decoder Models in Speech Recognitioncited by 0