Common Voice: A Massively-Multilingual Speech Corpus

TL;DR

The paper introduces the Common Voice corpus, a large, multilingual, publicly available speech dataset created via crowdsourcing, enabling advancements in speech recognition across numerous languages.

Contribution

It presents the largest multilingual speech corpus in the public domain and demonstrates its utility through speech recognition experiments with notable error rate improvements.

Findings

Largest public domain speech corpus with 2,500 hours and 38 languages

Transfer learning improved character error rates by approximately 6% on average

First published speech recognition results for several languages

Abstract

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.