The accents are sometimes tagged in the metadata csv files. So it’s possible to filter some of them out.

Mozilla DeepSpeech used to release checkpoints trained on Common Voice along with a few others (LibriSpeech etc). But they’ve dropped CV in the latest releases and just rely on the others.

I think the others are more standardised in terms of accents.

It’s likely possible to fine tune a model with different accents — so long as the language is the same then the model can up date the phonemes it recognises.

But accents are definitely a live issue.

In the worst case, you might have to treat different accents as different, but related languages. The current trend for low-resource languages seems to be about using one giant model for all languages to make use of shared features (e.g. for translation [1]), so adding even more languages might not be that expensive in terms of training data required.

[1] https://ai.googleblog.com/2019/10/exploring-massively-multil...

Isn't this literally what locales are for? Instead of "English" you have "En-US", "En-UK", "En-Ind" etc.

Locales can help distinguish country-specific variants (e.g. US vs. UK spelling), but pronunciation varies a lot at the sub-national level. If you want to support all possible accents, you'll need a more fine-grained encoding.

I think locales are for dialects, where you can have different terms used for the same concept. Here you can have someone speak the en-NZ dialect, but with a French accent.

Also, we would need en-FR, en-ES, en-IT, etc. All languages as spoken from all other native languages. And obviously the strength of the accent varies.

Yeah, also a Moravian (ancient nation united with the Czechs more than 1000 years ago) person will speak English differently than a Czech (as in nation, not state) person even though we all speak the Czech language.

A solution could be to tag both the dialect and the accent with language codes. Native speakers of Moravian Czech will probably have similar accents when they speak New Zealand English. Using Glottolog IDs as tags for example, this might be represented as { dialect:"newz1240", accent:"czec1259" }. If the program can already recognise the New Zealand English dialect and the Moravian Czech accent, it might then leverage both of those to recognise the speech of a Moravian person speaking New Zealand English.

I don't know if it helps, but I've recorded myself quite a bit. In my profile I could set a native language and accent, as well as additional language and accent.

There has to be that data somewhere, right?

https://i.imgur.com/Zfj9F5V.png

They have fields associated with the user for accent, but those data are not shown to the listener last I checked.

It is a real problem. I sometimes invite a coworker traveling from a different country home for dinner.