I can tell from your comment (and it's responses) that the language on our homepage is a bit confusing, so thank you for the feedback.

To answer you question: Common Voice is about building a collection of labelled voice data (ie. sentence clips w/ transcripts) that can be used to, for instance, train speech-to-text algorithms. Part of the goals of this project though is to figure out how this data can best help people build voice technology. So it's pretty open ended at this point.

Mozilla does have an open source speech-to-text engine [1] we are developing, and we hope one day to use the Common Voice data to train this engine. DeepSpeech and Common Voice are related, but separate projects, if that makes sense.

As for LibriSpeech, the DeepSpeech team at Mozilla does use this data for training. However, the language is pretty antiquated, and we only get about 1K hours of data, whereas you need about 10K hours to get to a decent accuracy (WER of 10% and below). Common Voice is about adding to public corpora like LibraSpeech, not replacing them.

1.) https://github.com/mozilla/DeepSpeech

Very interesting. I was not aware that there is Mozilla DeepSpeech (which implements the model from the same called paper DeepSpeech by Baidu, in TensorFlow). Note that the issue with DeepSpeech (the CTC model from the Baidu paper) is that it really needs a lot of training data to perform well (that is a generic property of CTC). If you use more conventional models (hybrid NN/HMM models), you can get very decent word-error-rate performance with only a few hundred hours of data. The advantage of DeepSpeech of course is that it is simpler and you don't need a lexicon (mapping words to its pronunciations, i.e. sequence of phonemes).

I would also not use voice technology as the generic term for speech recognition, text-to-speech, and whatever else you want to do with this data. Rather, speech technology is the common term to cover all of this (https://en.wikipedia.org/wiki/Speech_technology).

Noted. Again thanks for the feedback :)

Most people see "voice recognition" and think "speech recognition" I am guilty of this having never looked further into the term. I probably saw speech recognition and voice recognition and assumed they meant the same. At the very least now I know there's specifics to each term but I have a feeling most people don't. Maybe whoever wrote the page isn't an actual developer but just a volunteer writer?

Could they use the different voices to generate unique, natural-sounding voices for text-to-speech?

I really hope so.

All the text-to-speech software I've used has a generic sounding accent for the country (your choices are typically American, Australian, British, Canadian) but there's a lot more accents out there. The software isn't bad - it sounds realistic - but I wish it sounded more how I would like it to.

There's some software, e.g. Cepstral Dallas - https://www.cepstral.com/en/demos but it sounds too robotic to actually use and that voice isn't available for Linux so I only have it installed on my MacBook.

I guess a lot of developers at e.g. Apple live in CA so Siri is probably influenced by that.

> Speech recognition is about recognizing the speech, the spoken words. Voice recognition is about recognizing the speakers voice, i.e. identifying the speaker.

Perhaps they want to do both eventually (?) That could explain the name.

Common Voice is only about collecting a large public database of voices. We do have a separate project around speech-to-text [1]. We haven't done much work around speaker recognition (AFAIK) or voice synthesis, but they are both very interesting both from a technical and privacy related standpoint. That said, both are out of the scope of Common Voice (which is only about the data).

1.) https://github.com/mozilla/DeepSpeech