# Massively Multilingual Adversarial Speech Recognition

arXiv: https://arxiv.org/abs/1904.02210 · HTML: https://mlsanity.com/paper/1904.02210/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Scaling End-to-End Models for Large-Scale Multilingual ASR](https://mlsanity.com/paper/2104.14830/) (2021)
- [Exploiting Adapters for Cross-lingual Low-resource Speech Recognition](https://mlsanity.com/paper/2105.11905/) (2021)
- [Improving Massively Multilingual ASR With Auxiliary CTC Objectives](https://mlsanity.com/paper/2302.12829/) (2023)
- [Pseudo-Labeling for Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2111.00161/) (2021)
- [LAE: Language-Aware Encoder for Monolingual and Multilingual ASR](https://mlsanity.com/paper/2206.02093/) (2022)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages](https://mlsanity.com/paper/2305.12606/) (2023)
- [Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization](https://mlsanity.com/paper/2111.15016/) (2021)
- [Efficient Weight factorization for Multilingual Speech Recognition](https://mlsanity.com/paper/2105.03010/) (2021)
- [Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation](https://mlsanity.com/paper/2204.02470/) (2022)
- [Differentiable Allophone Graphs for Language-Universal Speech Recognition](https://mlsanity.com/paper/2107.11628/) (2021)
- [A Language Agnostic Multilingual Streaming On-Device ASR System](https://mlsanity.com/paper/2208.13916/) (2022)
- [Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity](https://mlsanity.com/paper/2111.01326/) (2021)
- [An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement](https://mlsanity.com/paper/2402.17189/) (2024)
- [A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base](https://mlsanity.com/paper/2405.11125/) (2024)
- [Scaling Up Deliberation for Multilingual ASR](https://mlsanity.com/paper/2210.05785/) (2022)
- [Unsupervised domain adaptation for speech recognition with unsupervised error correction](https://mlsanity.com/paper/2209.12043/) (2022)
- [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)
- [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://mlsanity.com/paper/2108.01280/) (2021)
- [Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training](https://mlsanity.com/paper/2303.00786/) (2023)
- [UML: A Universal Monolingual Output Layer for Multilingual ASR](https://mlsanity.com/paper/2302.11186/) (2023)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages](https://mlsanity.com/paper/2111.12276/) (2021)
- [Automatic Speech Recognition Advancements for Indigenous Languages of the Americas](https://mlsanity.com/paper/2404.08368/) (2024)
- [Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech](https://mlsanity.com/paper/2105.09742/) (2021)
- [Oral to Web: Digitizing 'Zero Resource'Languages of Bangladesh](https://mlsanity.com/paper/2603.05272/) (2026)
- [SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings](https://mlsanity.com/paper/2607.01238/) (2026)

## Abstract (arXiv)

We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.