Really complicated question, but considering the free world got wikipedia and openstreetmaps, I'd bet we'll find a way.

> Really complicated question, but considering the free world got wikipedia and openstreetmaps, I'd bet we'll find a way.

Both of those involve entering data about external things. Asking people to share their own data is another thing entirely—I suspect most people, me included, are much more suspicious about that.

> Imagine someone like Mozilla trying to collect hundreds of voice clips from each of tens of millions of users!!

They do, and it's working! https://commonvoice.mozilla.org/en

Except they have 12k hours of audio, when really they could do with 12B hours of audio...

Then you need a lot of people that listen to those 12B hours of audio, and multiple listeners agree for each chunk of audio that what is spoken corresponds to the transcript.

Lots of machine learning systems can use unsupervised and semi-supervised learning. Then nobody has to listen to and annotate all that audio.

Yes, but then you don't need Mozilla collecting read speech samples. You can just scrape any audio out there, run speech activity detection, and there you go.

Good point. I'm doing my part to contribute to it, though, not much else I can do!

Didn't Moz fire all the people who worked on this, and they have started their own project now?

Well speech recognition for personal use doesn't have to recognise everyone. In fact it's a feature, not a bug if it recognises only me as the user.

Not an expert on any of this, but wouldn’t already published content (public or proprietary) such as Youtube videos, audiobooks, tv interviews, movies, tv programs, radio programs, podcasts, etc. be useful and exempt from privacy concerns?

Do user collected clips have soemthing so special to the point that it’s critical to collect them?

Movies etc would need to be transcribed accurately to be useful for training and even then just provide a single sample for the specific item.

Another problem is that the models tend to get very very large for what I've seen. A gigabyte to 10s of gigabyes is an undesirable requirement on your local machine.

Not sure about others, but DeepSpeech also distributes a "lite" model that's much smaller and suitable for mobile devices. Not sure how its accuracy compares to the full model though.

With insane amounts of computation, making models much smaller while having minimal impacts on performance is possible.