--- title: "Common Voice — Ardila 2019" description: "Most of the world's languages lack openly licensed transcribed speech for training speech recognizers, and existing multilingual collections are either…" authors: ["Rosana Ardila", "Megan Branson", "Kelly Davis", "Michael Henretty", "Michael Kohler", "Josh Meyer", "Reuben Morais", "Lindsay Saunders", "Francis M. Tyers", "Gregor Weber"] year: 2019 venue: "arXiv:1912.06670" source: "https://arxiv.org/abs/1912.06670" doi: "10.48550/arxiv.1912.06670" arxiv: "1912.06670" licence: "arXiv — non-exclusive licence to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/ardila-2019-common-voice-massively-multilingual-speech/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ardila-2019-common-voice-massively-multilingual-speech/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/page/2/) / Common Voice: A Massively-Multilingual Speech Corpus # Common Voice: A Massively-Multilingual Speech Corpus **Authors:** Rosana Ardila, Megan Branson, [Kelly Davis](https://md.research.elamur.ai/free/en/authors/kelly-davis/), Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, [Francis M. Tyers](https://md.research.elamur.ai/free/en/authors/francis-m-tyers/), Gregor Weber · **Year:** 2019 · **Venue:** arXiv:1912.06670 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/1912.06670) · [PDF](https://arxiv.org/pdf/1912.06670) · [DOI 10.48550/arxiv.1912.06670](https://doi.org/10.48550/arxiv.1912.06670) · [arXiv 1912.06670](https://arxiv.org/abs/1912.06670) · [OpenAlex](https://openalex.org/W2995929068) **Citations:** 199 citations (openalex-snapshot · 2026-10-14) · [531](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Most of the world's languages lack openly licensed transcribed speech for training speech recognizers, and existing multilingual collections are either restrictively licensed, have no quality control, or have no community behind them. Mozilla's Common Voice tackles this with crowdsourcing. Volunteers record themselves reading prompt sentences on a website or iPhone app, and other volunteers vote on whether each clip matches its text. Any community can add its language by localizing the interface and gathering a pool of public-domain sentences. The resulting corpus is released under CC0 and, by the authors' account, is the largest public-domain ASR corpus in both hours and number of languages. To show its usefulness, the authors fine-tune an English DeepSpeech model on twelve other languages. Transfer learning lowers Character Error Rate on average, and the majority of the languages tested had never before had end-to-end ASR results published. The project offers a scalable and sustainable route to speech data for low-resource languages. ## Key points - **Scale:** The June 2019 release covers 29 languages, and 38 language communities were collecting data by November 2019. Contributors number over 50,000, with roughly 2,500 hours recorded and about 2,000 hours validated. - **Validation:** - Up to three listeners judge each clip; two up-votes make it valid and two down-votes make it invalid. - Train, dev and test splits are speaker-disjoint, with sizes set by power analysis (99% confidence, 1% margin). - Audio is distributed as 48kHz mono MP3. - **Adding a language:** - The roughly 610 interface strings are translated in Pontoon. - At least 5,000 sentences must be gathered, either through the Sentence Collector or by extraction from Wikipedia. - **Experiments:** - The model is DeepSpeech v0.3.0, a 6-layer CTC network with one LSTM layer. - Layers are transferred from an English model to 12 languages. - Training data ranges from under 1,000 clips (Slovenian) to over 65,000 clips (German). - **Results:** Mean CER improvement is 5.99 ± 5.48. Copying four English layers performed best overall. The largest gain was for Chuvash, where CER fell from 48.1 to 27.21. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/) ## Cited by Cited by 531 papers of our library cite it (of the 140673 whose bibliographies we read); 51 of them have a page here, 480 not yet. - [Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/abdallah-2023-leveraging-data-collection-unsupervised-learning/) - [Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic](https://md.research.elamur.ai/free/en/papers/acl-2024-kheir-beyond-orthography-automatic-recovery-short/) - [FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN](https://md.research.elamur.ai/free/en/papers/agarwal-2023-findings-iwslt-2023-evaluation-campaign/) - [Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task](https://md.research.elamur.ai/free/en/papers/al-2025-shaibani-poem-meter-classification-recited-arabic/) - [Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings](https://md.research.elamur.ai/free/en/papers/al-2025-shwayyat-hybrid-deep-learning-signal-processing/) - [Universal Dependencies for Saraiki](https://md.research.elamur.ai/free/en/papers/alam-2024-universal-dependencies-saraiki/) - [Arabic Little STT: Arabic Children Speech Recognition Dataset](https://md.research.elamur.ai/free/en/papers/alkadri-2025-arabic-little-stt-arabic-children/) - [Findings of the IWSLT 2022 Evaluation Campaign](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2022-findings-iwslt-2022-evaluation-campaign/) - [Bulbul: A Dataset for Dialectal Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/ashraf-2026-bulbul-dataset-dialectal-arabic-speech/) - [Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation](https://md.research.elamur.ai/free/en/papers/baali-2023-arabic-dysarthric-speech-recognition-using/) - 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[High Fidelity Neural Audio Compression](https://md.research.elamur.ai/free/en/papers/defossez-2022-high-fidelity-neural-audio-compression/) - [Ara-Best-RQ: Multi Dialectal Arabic SSL](https://md.research.elamur.ai/free/en/papers/elleuch-2026-ara-best-rq-multi-dialectal/) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://md.research.elamur.ai/free/en/papers/gemini-2024-team-gemini-1-5-unlocking-multimodal/) - [Almieyar: A Culturally Grounded Benchmark for Multi-Dialect Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/ghahroodi-2026-almieyar-culturally-grounded-benchmark-multi/) - [Open Automatic Speech Recognition Models for Classical and Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/grigoryan-2025-open-automatic-speech-recognition-models/) - [GlotLID: Language Identification for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/) - [Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study](https://md.research.elamur.ai/free/en/papers/kheir-2025-towards-unified-benchmark-arabic-pronunciation/) - [NoLoR: An ASR-Based Framework for Expedited Endangered Language Documentation with Neo-Aramaic as a Case Study](https://md.research.elamur.ai/free/en/papers/langstream-2024-nazari-nolor-asr-based-framework-expedited/) - [Phonetic-based Ranking for Improved Pseudo-Labeling in Low-Resource ASR](https://md.research.elamur.ai/free/en/papers/matassoni-2026-phonetic-based-ranking-improved-pseudo/) - [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/) - [Central Kurdish Text-to-Speech and Its Application in Speech-to-Text Translation](https://md.research.elamur.ai/free/en/papers/mohammadamini-2026-central-kurdish-text-speech-its/) - [Fleurs-Badini: Translation and Recording Fleurs Dataset for Badini Variant of Northern Kurdish](https://md.research.elamur.ai/free/en/papers/mohammadamini-2026-fleurs-badini-translation-recording-fleurs/) - [Southern Kurdish Speech Recognition Resources and Benchmarking](https://md.research.elamur.ai/free/en/papers/mohammadamini-2026-southern-kurdish-speech-recognition-resources/) - [Fine-tuning Whisper with Spontaneous Persian Speech (SPS)](https://md.research.elamur.ai/free/en/papers/namdarzadeh-2026-fine-tuning-whisper-spontaneous-persian/) - [Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning](https://md.research.elamur.ai/free/en/papers/ozyilmaz-2025-overcoming-data-scarcity-multi-dialectal/) - [BSC's Submission to the Instruction Following Track of IWSLT 2026](https://md.research.elamur.ai/free/en/papers/pareras-2026-bsc-s-submission-instruction-following/) - [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://md.research.elamur.ai/free/en/papers/pratap-2020-mls-large-scale-multilingual-dataset/) - [Scaling Speech Technology to 1,000+ Languages](https://md.research.elamur.ai/free/en/papers/pratap-2023-scaling-speech-technology-1-000/) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/) - [AudioPaLM: A Large Language Model That Can Speak and Listen](https://md.research.elamur.ai/free/en/papers/rubenstein-2023-audiopalm-large-language-model-that/) - [Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers](https://md.research.elamur.ai/free/en/papers/salameh-2024-quranic-audio-dataset-crowdsourced-labeled/) - [A Sociophonetic Analysis of Racial Bias in Commercial ASR Systems Using the Pacific Northwest English Corpus](https://md.research.elamur.ai/free/en/papers/scott-2026-sociophonetic-analysis-racial-bias-commercial/) - [MUSCAT: MUltilingual, SCientific ConversATion Benchmark](https://md.research.elamur.ai/free/en/papers/sinhamahapatra-2026-muscat-multilingual-scientific-conversation-benchmark/) - [N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/talafha-2023-n-shot-benchmarking-whisper-diverse/) - [FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions](https://md.research.elamur.ai/free/en/papers/teixeira-2026-falar-large-scale-speaker-annotated/) - [Building Collaborative Speech Corpora for Low-Resource Languages: The Galician Dataset in Mozilla Common Voice](https://md.research.elamur.ai/free/en/papers/vladu-2026-building-collaborative-speech-corpora-low/) - [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://md.research.elamur.ai/free/en/papers/wang-2021-voxpopuli-large-scale-multilingual-speech/) - [Open Universal Arabic ASR Leaderboard](https://md.research.elamur.ai/free/en/papers/wang-2024-open-universal-arabic-asr-leaderboard/) - [Nuha-Speech: Building General-Purpose Arabic Speech-LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-nuha-speech-building-general-purpose/) - [UM-DFKI Maltese Speech Translation](https://md.research.elamur.ai/free/en/papers/williams-2023-um-dfki-maltese-speech-translation/) - [CS-YODAS: A Mined Dataset of In-the-Wild Code-Switched Speech](https://md.research.elamur.ai/free/en/papers/yan-2026-cs-yodas-mined-dataset-wild/) - [CUHKSZ Simultaneous Speech Translation System for IWSLT 2026](https://md.research.elamur.ai/free/en/papers/yang-2026-cuhksz-simultaneous-speech-translation-system/) - [TG-ASR: Translation-Guided Learning with Parallel Gated Cross Attention for Low-Resource Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/yang-2026-tg-asr-translation-guided-learning/) - [CATENG Submission for the IWSLT 2026: Dialectal and Low-resource Speech Translation Task](https://md.research.elamur.ai/free/en/papers/zevallos-2026-cateng-submission-iwslt-2026-dialectal/) - [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://md.research.elamur.ai/free/en/papers/zhang-2023-speechgpt-empowering-large-language-models/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{ardila2019common, title = {{Common Voice: A Massively-Multilingual Speech Corpus}}, author = {Ardila, Rosana and Branson, Megan and Davis, Kelly and Henretty, Michael and Kohler, Michael and Meyer, Josh and Morais, Reuben and Saunders, Lindsay and Tyers, Francis M. and Weber, Gregor}, year = {2019}, doi = {10.48550/arxiv.1912.06670}, eprint = {1912.06670}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.1912.06670} } ``` - [← previous COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) - [next → compare-mt: A Tool for Holistic Comparison of Language Generation Systems](https://md.research.elamur.ai/free/en/papers/neubig-2019-compare-mt-tool-holistic-comparison/) - [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/page/2/) ## References 9 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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