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

arXiv: https://arxiv.org/abs/2409.10429 · HTML: https://mlsanity.com/paper/2409.10429/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [SeamlessM4T: Massively Multilingual & Multimodal Machine Translation](https://mlsanity.com/paper/2308.11596/) (2023)
- [ML-SUPERB: Multilingual Speech Universal PERformance Benchmark](https://mlsanity.com/paper/2305.10615/) (2023)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR](https://mlsanity.com/paper/2406.06619/) (2024)
- [Can Whisper perform speech-based in-context learning?](https://mlsanity.com/paper/2309.07081/) (2023)
- [Meta Learning for End-to-End Low-Resource Speech Recognition](https://mlsanity.com/paper/1910.12094/) (2019)
- [Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning](https://mlsanity.com/paper/2305.15212/) (2023)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [Can Contextual Biasing Remain Effective with Whisper and GPT-2?](https://mlsanity.com/paper/2306.01942/) (2023)
- [Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2203.14222/) (2022)
- [Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition](https://mlsanity.com/paper/2012.11896/) (2020)
- [Massively Multilingual Shallow Fusion with Large Language Models](https://mlsanity.com/paper/2302.08917/) (2023)
- [A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets](https://mlsanity.com/paper/2403.03909/) (2024)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [A Novel Self-training Approach for Low-resource Speech Recognition](https://mlsanity.com/paper/2308.05269/) (2023)
- [Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning](https://mlsanity.com/paper/2106.04911/) (2021)
- [Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling](https://mlsanity.com/paper/2408.14026/) (2024)
- [Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review](https://mlsanity.com/paper/2408.10330/) (2024)
- [Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition](https://mlsanity.com/paper/2310.11010/) (2023)

## Built on by (most cited first, 4 of 4)

- [IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages](https://mlsanity.com/paper/2606.19157/) (2026)
- [Advancing STT for Low-Resource Real-World Speech](https://mlsanity.com/paper/2506.08836/) (2025)
- [MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learning](https://mlsanity.com/paper/2601.18904/) (2026)
- [In-Context Adaptation of Encoder-Decoder Models in Speech Recognition](https://mlsanity.com/paper/2609.33865/) (2026)

## 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.