Many people are excited about creating usable speech technology. However, most of the audio data used by large companies isn’t available to the majority of people, and that data is often biased in terms of language, accent, and gender. Jenny, Josh, and Remy from Mozilla join us to discuss how Mozilla is building an open-source voice database that anyone can use to make innovative apps for devices and the web (Common Voice). They also discuss efforts through Mozilla fellowship program to develop speech tech for African languages and understand bias in data sets.

Discuss on Changelog News

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Featuring

• Jenny Zhang – Twitter, Website
• Remy Muhire – Twitter, GitHub
• Josh Meyer – Twitter, GitHub
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Notes and Links

• Mozilla Common Voice
• Announcement of Josh and Remy’s fellowship work on speech tech for African languages
• Artie Bias Corpus
• Readings on Demographic Bias in ASR:
  • Voice recognition still has significant race and gender biases
  • Gender and Dialect Bias in YouTube’s Automatic Captions
  • Racial disparities in automated speech recognition
• Common Voice LREC Paper
• Common Voice + DeepSpeech collaborators for Low-resource languages:
  • Digital Umuganda
  • AI Lab, Makerere University
  • Language Technologies Unit, Bangor University
  • Linguistics Department, Indiana University Bloomington
• “under-sampled majority” is a quote from Joy Boulamwini (see this article)

Something missing or broken? PRs welcome!