# A Survey of Multilingual Models for Automatic Speech Recognition arXiv: https://arxiv.org/abs/2202.12576 ยท HTML: https://mlsanity.com/paper/2202.12576/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 51 references that are in the map. - First posted on arXiv 25 Feb 2022. ## Builds on (most cited first, 28 of 28) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021) - [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020) - [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020) - [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020) - [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015) - [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014) - [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021) - [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019) - [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) - [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (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) - [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://mlsanity.com/paper/2101.07597/) (2021) - [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://mlsanity.com/paper/1904.03288/) (2019) - [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016) - [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021) - [Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages](https://mlsanity.com/paper/1806.05059/) (2018) - [CLSRIL-23: Cross Lingual Speech Representations for Indic Languages](https://mlsanity.com/paper/2107.07402/) (2021) - [XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition](https://mlsanity.com/paper/2103.08207/) (2021) - [Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/2104.09106/) (2021) - [Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model](https://mlsanity.com/paper/1711.10025/) (2017) - [Phonemic and Graphemic Multilingual CTC Based Speech Recognition](https://mlsanity.com/paper/1711.04564/) (2017) - [Improving low-resource ASR performance with untranscribed out-of-domain data](https://mlsanity.com/paper/2106.01227/) (2021) ## Built on by (most cited first, 21 of 21) - [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023) - [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024) - [The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It](https://mlsanity.com/paper/2505.24119/) (2025) - [Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes](https://mlsanity.com/paper/2306.04306/) (2023) - [Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning](https://mlsanity.com/paper/2306.15686/) (2023) - [Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context](https://mlsanity.com/paper/2404.02000/) (2024) - [How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario](https://mlsanity.com/paper/2411.18217/) (2024) - [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025) - [Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi](https://mlsanity.com/paper/2206.12931/) (2022) - [A Parameter-efficient Language Extension Framework for Multilingual ASR](https://mlsanity.com/paper/2406.06329/) (2024) - [HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing](https://mlsanity.com/paper/2407.07566/) (2024) - [Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication](https://mlsanity.com/paper/2306.00614/) (2023) - [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024) - [Sagalee: an Open Source Automatic Speech Recognition Dataset for Oromo Language](https://mlsanity.com/paper/2502.00421/) (2025) - [Towards a World-English Language Model for On-Device Virtual Assistants](https://mlsanity.com/paper/2403.18783/) (2024) - [Survey: Transformer-based Models in Data Modality Conversion](https://mlsanity.com/paper/2408.04723/) (2024) - [Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR](https://mlsanity.com/paper/2410.13445/) (2024) - [Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts](https://mlsanity.com/paper/2410.18444/) (2024) - [SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation](https://mlsanity.com/paper/2510.00582/) (2025) - [Continual Adaptation for Pacific Indigenous Speech Recognition](https://mlsanity.com/paper/2603.06310/) (2026) - [Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking](https://mlsanity.com/paper/2605.30107/) (2026) ## Abstract (arXiv) Although Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, the majority of the world's languages do not have usable systems due to the lack of large speech datasets to train these models. Cross-lingual transfer is an attractive solution to this problem, because low-resource languages can potentially benefit from higher-resource languages either through transfer learning, or being jointly trained in the same multilingual model. The problem of cross-lingual transfer has been well studied in ASR, however, recent advances in Self Supervised Learning are opening up avenues for unlabeled speech data to be used in multilingual ASR models, which can pave the way for improved performance on low-resource languages. In this paper, we survey the state of the art in multilingual ASR models that are built with cross-lingual transfer in mind. We present best practices for building multilingual models from research across diverse languages and techniques, discuss open questions and provide recommendations for future work.