  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.
