# Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages

arXiv: https://arxiv.org/abs/2511.09690 · HTML: https://mlsanity.com/paper/2511.09690/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2025.

## Built on by (most cited first, 30 of 34)

- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [HuPER: A Human-Inspired Framework for Phonetic Perception](https://mlsanity.com/paper/2602.01634/) (2026)
- [Ethio-ASR: Joint Multilingual Speech Recognition and Language Identification for Ethiopian Languages](https://mlsanity.com/paper/2603.23654/) (2026)
- [Benchmarking Multilingual Speech Models on Pashto: Zero-Shot ASR, Script Failure, and Cross-Domain Evaluation](https://mlsanity.com/paper/2604.04598/) (2026)
- [Kakugo: Distillation of Low-Resource Languages into Small Language Models](https://mlsanity.com/paper/2601.14051/) (2026)
- [Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2602.01008/) (2026)
- [Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition](https://mlsanity.com/paper/2603.17558/) (2026)
- [Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment](https://mlsanity.com/paper/2604.06191/) (2026)
- [The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models](https://mlsanity.com/paper/2604.20225/) (2026)
- [Zero-Shot Context-Aware ASR for Diverse Arabic Varieties](https://mlsanity.com/paper/2511.18774/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)
- [BYOL: Bring Your Own Language Into LLMs](https://mlsanity.com/paper/2601.10804/) (2026)
- [Continual Adaptation for Pacific Indigenous Speech Recognition](https://mlsanity.com/paper/2603.06310/) (2026)
- [Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech](https://mlsanity.com/paper/2603.07513/) (2026)
- [FLEURS-Kobani: Extending the FLEURS Dataset for Northern Kurdish](https://mlsanity.com/paper/2603.29892/) (2026)
- [AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition](https://mlsanity.com/paper/2605.03590/) (2026)
- [The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints](https://mlsanity.com/paper/2605.19066/) (2026)
- [FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations](https://mlsanity.com/paper/2606.02615/) (2026)
- [Pretrained self-supervised speech models can recognize unseen consonants](https://mlsanity.com/paper/2606.11542/) (2026)
- [UR-BERT: Scaling Text Encoders for Massively Multilingual TTS Through Universal Romanization and Speech Token Prediction](https://mlsanity.com/paper/2606.11681/) (2026)
- [Comparing Human and Automatic Recognition of Dutch Dysarthric Continuous Speech: A Case Study](https://mlsanity.com/paper/2606.30237/) (2026)
- [S-DiverSe: Spanish Diverse Speech](https://mlsanity.com/paper/2607.03207/) (2026)
- [GigaAM Multilingual: Foundation Model for Underrepresented Languages](https://mlsanity.com/paper/2607.10371/) (2026)
- [An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge](https://mlsanity.com/paper/2607.12468/) (2026)
- [MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2607.24030/) (2026)
- [Easper: An Accessible ASR Pipeline for Language Documentation](https://mlsanity.com/paper/2608.11629/) (2026)
- [Alignment-Free Text-Audiobox for Voice Dubbing and Full-Duplex Dialogue Synthesis](https://mlsanity.com/paper/2609.03992/) (2026)
- [YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speech](https://mlsanity.com/paper/2609.29448/) (2026)
- [In-Context Adaptation of Encoder-Decoder Models in Speech Recognition](https://mlsanity.com/paper/2609.33865/) (2026)
- [CARDAMOM: A Micro-Dialectal Arabic Speech Dataset for ASR](https://mlsanity.com/paper/2609.34481/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages behind. Expanding ASR coverage has been costly and limited by architectures that restrict language support, making extension inaccessible to most--all while entangled with ethical concerns when pursued without community collaboration. To transcend these limitations, we introduce Omnilingual ASR, the first large-scale ASR system designed for extensibility. Omnilingual ASR enables communities to introduce unserved languages with only a handful of data samples. It scales self-supervised pre-training to 7B parameters to learn robust speech representations and introduces an encoder-decoder architecture designed for zero-shot generalization, leveraging a LLM-inspired decoder. This capability is grounded in a massive and diverse training corpus; by combining breadth of coverage with linguistic variety, the model learns representations robust enough to adapt to unseen languages. Incorporating public resources with community-sourced recordings gathered through compensated local partnerships, Omnilingual ASR expands coverage to over 1,600 languages, the largest such effort to date--including over 500 never before served by ASR. Automatic evaluations show substantial gains over prior systems, especially in low-resource conditions, and strong generalization. We release Omnilingual ASR as a family of models, from 300M variants for low-power devices to 7B for maximum accuracy. We reflect on the ethical considerations shaping this design and conclude by discussing its societal impact. In particular, we highlight how open-sourcing models and tools can lower barriers for researchers and communities, inviting new forms of participation. Open-source artifacts are available at https://github.com/facebookresearch/omnilingual-asr.