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If you're a #language nerd like I am, then you won't have missed the @mozilla #CommonVoice v19 #speech #dataset release - which now features 131 languages! Here's my #dataviz, done in @observablehq of the v19 #metadata coverage.
I've updated the visualisation this time around with human-readable language names instead of their ISO-639 or BCP-47 language codes to make it it easier to read.
There's some interesting observations:
▶ Catalan (ca) continues to be leader in terms of data - speaking volumes about the efforts to revitalise culture and language in Catalunya. It's also one of the few languages that has data for all age groups, particularly older speakers - this sort of data is missing for most other languages.
▶ Kiswahili (sw) is one of the languages where there is more data for female-identifying speakers than for male-identifying speakers ♀ - although Japanese (ja), Western Mari (mrj) and Luganda (lg) do pretty well here, too!
▶ Sentence domains can now be categorised, and although most new sentences are "general", Albanian (sq) has a lot of sentences related to law and government.
▶ Tsonga (ts), a Bantu language spoken in Southern Africa, has dethroned Icelandic (is) as the language with the highest average utterance duration. I don't know enough about Tsonga to speculate why - it's a somewhat agglutinative language, but many Tsonga works are generally short.
▶ Bengali / Bangla (bn) has a significant amount of data that is not yet validated, and therefore does not appear in training / dev / test splits. There is a similar case for many languages new to Common Voice - it takes time to validate.
▶ The language with the highest number of average contributions per speaker is Taita (dav), a Bantu language from Kenya.
What do you make of the data visualisation? Are there any other insights you can see?
Big thanks to the CV team for all their efforts - EM, Jessica Rose, Dmitrij Feller and Justin Grant.
📣 ¡Emocionantes noticias! Common Voice, de Mozilla, nos invita a un emocionante proyecto.
Localizamos el wayuunaiki en una base de datos pública para que los desarrolladores creen aplicaciones de reconocimiento de voz en lenguas de bajos recursos. ¡Únete a la comunidad wayuunaiki-tic y hagamos que el wayuunaiki se escuche en la era digital!
🌐Más info: chat.whatsapp.com/FmAesbq8m8Q9…
We used this for teaching statistics (#rstats). Students should find out if there are gender differences by counting male vs. female contributors. There were significantly more men in our sample. Bad for unbiased #AI! If more use such projects, we can also grow the open datasets faster and fairer. #genderinequality
