mlsanityData to 14 Oct 2026

Common Voice: A Massively-Multilingual Speech Corpus

arXiv 1912.06670 ↗First posted 13 Dec 2019cs.CL cs.LGcited by 1,384

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Cited by 33 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla's DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of 5.99 +/- 5.48 for twelve target languages (German, French, Italian, Turkish, Catalan, Slovenian, Welsh, Irish, Breton, Tatar, Chuvash, and Kabyle). For most of these languages, these are the first ever published results on end-to-end Automatic Speech Recognition.

Key facts. Cited by 1,384 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2019.

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Citing papers per year: 2021: 107; 2022: 133; 2023: 198; 2024: 292; 2025: 336; 2026: 318.

1,384 papers.

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  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2022High Fidelity Neural Audio Compressioncited by 687
  3. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
  4. 2024Qwen2-Audio Technical Reportcited by 560
  5. 2023SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilitiescited by 529
  6. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
  7. 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 463
  8. 2023High-Fidelity Audio Compression with Improved RVQGANcited by 431
  9. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  10. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
  11. 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 379
  12. 2024F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matchingcited by 361
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  1. 2023AudioPaLM: A Large Language Model That Can Speak and Listencited by 343
  2. 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 278
  3. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  4. 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 231
  5. 2025Kimi-Audio Technical Reportcited by 222
  6. 2024AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehensioncited by 220
  7. 2024AnyGPT: Unified Multimodal LLM with Discrete Sequence Modelingcited by 217
  8. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 202
  9. 2024VoiceBench: Benchmarking LLM-Based Voice Assistantscited by 200
  10. 2023Multimodal Large Language Models: A Surveycited by 195
  11. 2021A Survey on Neural Speech Synthesiscited by 184
  12. 2024XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Modelcited by 179
  13. 2026Qwen3.5-Omni Technical Reportcited by 174
  14. 2024MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformercited by 161
  15. 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 151
  16. 2025CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-trainingcited by 150
  17. 2021Unsupervised Speech Recognitioncited by 148
  18. 2023Scaling Laws for Generative Mixed-Modal Language Modelscited by 134
  19. 2024WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modelingcited by 129
  20. 2024AudioBench: A Universal Benchmark for Audio Large Language Modelscited by 128
  21. 2024Spirit LM: Interleaved Spoken and Written Language Modelcited by 126
  22. 2024FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMscited by 122
  23. 2021Textless Speech-to-Speech Translation on Real Datacited by 121
  24. 2023Audiobox: Unified Audio Generation with Natural Language Promptscited by 120
  25. 2026Qwen3-ASR Technical Reportcited by 118
  26. 2023Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labelscited by 114
  27. 2024Large Language Models for Time Series: A Surveycited by 114
  28. 2025Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokenscited by 113
  29. 2022mSLAM: Massively multilingual joint pre-training for speech and textcited by 108
  30. 2023SONAR: Sentence-Level Multimodal and Language-Agnostic Representationscited by 108
  31. 2024Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Expertscited by 108
  32. 2025MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmarkcited by 103
  33. 2021The Multilingual TEDx Corpus for Speech Recognition and Translationcited by 101
  34. 2021The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usagecited by 101
  35. 2023Textually Pretrained Speech Language Modelscited by 101
  36. 2023EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesiscited by 98
  37. 2023Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLMcited by 94
  38. 2023Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labellingcited by 90

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