# Pseudo-Labeling for Massively Multilingual Speech Recognition

arXiv: https://arxiv.org/abs/2111.00161 · HTML: https://mlsanity.com/paper/2111.00161/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [Improved Noisy Student Training for Automatic Speech Recognition](https://mlsanity.com/paper/2005.09629/) (2020)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://mlsanity.com/paper/1911.08460/) (2019)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model](https://mlsanity.com/paper/1909.05330/) (2019)
- [Multilingual Speech Recognition With A Single End-To-End Model](https://mlsanity.com/paper/1711.01694/) (2017)
- [Multi-Task Self-Training for Learning General Representations](https://mlsanity.com/paper/2108.11353/) (2021)
- [Iterative Pseudo-Labeling for Speech Recognition](https://mlsanity.com/paper/2005.09267/) (2020)
- [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020)
- [Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes](https://mlsanity.com/paper/1811.09021/) (2018)
- [SlimIPL: Language-Model-Free Iterative Pseudo-Labeling](https://mlsanity.com/paper/2010.11524/) (2020)
- [CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings](https://mlsanity.com/paper/2106.03143/) (2021)
- [Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition](https://mlsanity.com/paper/2106.08922/) (2021)
- [Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling](https://mlsanity.com/paper/1810.03459/) (2018)
- [Massively Multilingual Adversarial Speech Recognition](https://mlsanity.com/paper/1904.02210/) (2019)
- [RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions](https://mlsanity.com/paper/2005.03271/) (2020)
- [Phonemic and Graphemic Multilingual CTC Based Speech Recognition](https://mlsanity.com/paper/1711.04564/) (2017)

## Built on by (most cited first, 24 of 24)

- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement](https://mlsanity.com/paper/2406.11546/) (2024)
- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages](https://mlsanity.com/paper/2305.12606/) (2023)
- [CL-MASR: A Continual Learning Benchmark for Multilingual ASR](https://mlsanity.com/paper/2310.16931/) (2023)
- [Towards Zero-Shot Code-Switched Speech Recognition](https://mlsanity.com/paper/2211.01458/) (2022)
- [Boosting Cross-Domain Speech Recognition with Self-Supervision](https://mlsanity.com/paper/2206.09783/) (2022)
- [A Novel Self-training Approach for Low-resource Speech Recognition](https://mlsanity.com/paper/2308.05269/) (2023)
- [Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation](https://mlsanity.com/paper/2210.12214/) (2022)
- [Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation](https://mlsanity.com/paper/2312.03312/) (2023)
- [Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling](https://mlsanity.com/paper/2408.14026/) (2024)
- [Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering](https://mlsanity.com/paper/2506.03681/) (2025)
- [Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models](https://mlsanity.com/paper/2209.13523/) (2022)
- [Token-Weighted RNN-T for Learning from Flawed Data](https://mlsanity.com/paper/2406.18108/) (2024)
- [NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data](https://mlsanity.com/paper/2507.00534/) (2025)
- [ManWav: The First Manchu ASR Model](https://mlsanity.com/paper/2406.13502/) (2024)
- [Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper](https://mlsanity.com/paper/2409.13499/) (2024)
- [WorldSpeech: A Multilingual Speech Corpus from Around the World](https://mlsanity.com/paper/2605.09167/) (2026)
- [Unsupervised ASR via Cross-Lingual Pseudo-Labeling](https://mlsanity.com/paper/2305.13330/) (2023)
- [Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice](https://mlsanity.com/paper/2508.20717/) (2025)
- [Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning](https://mlsanity.com/paper/2603.06505/) (2026)
- [The Exact Worst-Case Tail Probability under Bounded Kurtosis](https://mlsanity.com/paper/2607.05226/) (2026)
- [Look Less, Hear Better: Jointly Rewarded GRPO for Streaming ASR](https://mlsanity.com/paper/2609.18333/) (2026)
- [Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR](https://mlsanity.com/paper/2610.13807/) (2026)

## Abstract (arXiv)

Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised learning on a target language, generate pseudo-labels for that language, and train a final model using pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better performance for many languages that also transfers well to LibriSpeech.