Iām specifically talking about issues like āhereā in place of āhearā, which I have seen in the text.
Got it, well I donāt know about this one.
We now have the Sentence Collector which provides a way to submit new sentences and have them validated. However, it has only been live for the past few weeks and many of the sentences pre-date that. In fact, English currently has a big backlog so you are reviewing sentences recorded around a year ago.
I believe the eventual plan is to resubmit the existing sentences through the Sentence Collector so they can be validated and fixed as needed.
We want to run an automated clean-up an on the existing sentences. We havenāt established yet a final process to solve the issue described here, the current one is to request the removal of these sentences and then re-submit them corrected to the sentence collector tool.
Any ideas to improve this process are welcomed! 
How many are we talking about? A good proof-reader should be able to check and edit at quite a speed; much quicker than it takes to speak them.
āHearā and āhereā might be a distracting error in print, but would not necessarily affect the spoken words. Leonardo Di Caprio āstaringā in a film would, however.
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In my area, thereās a big difference between āhearā (heer) and here (hee-ya).
Even if it doesnāt make a difference to pronunciation, sometimes speakers pause or stumble when they encounter a mistake in the text.
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We need a tool that lets you
- search in all submitted sentences of a specified language
- submit a corrected version of that sentence
- provide a justification for the correction (e.g. a link to a dictionary)
- review the corrections that others made (ideally allowing a discussion between corrector and reviewer)
I already collected dozens of mistakes in the German corpus. The longer we wait the more of them make it into it.
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I donāt expect we will be able to have the tool to do what you describe in the short term (we have other priorities and not a lot of resources), thatās why the current proposal to at least ensure no bad sentences end up in the dataset is what I described:
- Request removal from the sentences list.
- Correct the sentences and submit them to the sentence collector so they end up in the site with their right form.
Existing sentences that can be identified automatically as āwrongā will be removed by our cleaning scripts.
In any case your feedback is valuable and we can incorporate it into the list of things we would like to have in the future.
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The lack of resources is understandable. Where and how do I request the removal of several sentences? Shall I make a PR on Github?
Yes please, thanks for your understanding
Suggestion: since the volunteer readers will all be seeing these sentences and having to inspect them attentively (we hope) before reading them aloud, could we in addition to the button saying āSkipā - provide a button for āError in this sentence?ā That should act as a filter to collect as many as possible, narrowing the task for the second-level reviewers. It wonāt be infallible, but it could be a big (and inexpensive) step in the right direction.
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Incidentally, you speak of needing to cite a reference to justify corrections. That sounds needlessly complex to me, and would be very difficult in the case of punctuation and some of the grammar errors. Youāll find plenty of people who can identify that āshouldā needs to be replaced with āwillā but far fewer who will be able to identify it as an inappropriate use of the future conditional subjunctive (for example). Why do you need that anyway?
Couldnāt it just be a system where people vote on whether they agree with the correction? Thatās much simpler.
I suggest we donāt go too deep into solution ideation, since at the end of the day itās not something we will be able to change today, and sometimes it tends to be an endless conversation about personal preferences.
I think itās better to focus on describing the problem clearly so we can come back here for reference when we have time to start thinking on a proper solution
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Hereās a question: at what point is it no longer useful to get additional recordings of a sentence? I know DeepSpeech only uses one recording of a sentence. Even if someone else can make use of additional copies, there must surely be a point of diminishing return.
So essentially my question is: is it worth even bothering to import the old sentences that have existed for years and been recorded countless times already? Maybe they should just be retired once Sentence Collector has amassed a certain number of new sentences.
Hereās the situation I encountered while reviewing Greek sentences: it seems that the ones in there are fragments from a number of books. I couldnāt recognize the source of the ones I got just now (āĪĻ ĻĪæĻĪ¼Īµ ĻĪæ Ī¬Ī»ĻĪ± ĪµĪÆĪ½Ī±Ī¹ ĻĪæ ĻĪ¹Īæ ĻĻ ĻĪµĻĻā, āĪĻĪæĻĪµĪÆ Ī½Ī± Ī¶ĪµĻ Ī³Ī±ĻĻĻĪµĪ¹ Ī¼Īµ ĪĪ½Ī± ĻĻĻĻ Ī¬Ī»Ī»Ī± Ī³ĻĪ±Ī¼Ī¼Ī±ĻĪ¬ĪŗĪ¹Ī±ā, āĪŗĪ±Ī¹ ĻĻĪ¼ĻĻĪ½Ī± ĪŗĪ±Ī¹ ĻĻĪ½Ī®ĪµĪ½ĻĪ±ā) but itās obvious that itās a sentence from some existing text cut in three pieces at punctuation boundaries. This creates pieces of grammatically correct but incomplete language and, of course, not sentences that would appear in a regular speech corpus.
I found the source of couple of other sentences I got: itās from the book āĪ Ī±ĻĪ±Ī¼ĻĪøĪ¹ ĻĻĻĪÆĻ ĻĪ½ĪæĪ¼Ī±ā which seems to be in the public domain (the author died in 1941) but it suffers from the same problem.
Iām not sure it makes sense to continue reviewing Greek sentences if the whole set is like thisā¦ I could just downvote everything thatās not a complete sentence, but even those seem ill-fit for the purpose and, if we canāt recognize the source, there could be copyright problems with them.
Might some āquick and dirtyā automated screening of sentences be possible by use of the kind of language models used by DeepSpeech on the sentences submitted?
KenLM etc work by giving you the probability of a sentence and if the model is big enough to be representative then it should get that probability reasonably accurately and in turn might you find a threshold below which things are either excluded or flagged to the user.
Typically they would give sentences with spelling mistakes much lower scores. There is some risk of legitimate sentences being hit, but perhaps at the margins thatās not such a big deal (since this is to produce a dataset for pronunciation not to represent every phrase possible)
These models tend to be quick to run (for the probabilities, not to create), so am hoping it could be tacked on without killing the user experience. And whilst I agree something sophisticated like @jf99 envisages would be great, with the resources / time available, āgood enoughā may be more practical 
Finally, I see some LMs also let you see the probabilities of individual words within a sentence, such as here: https://colinmorris.github.io/lm-sentences/#/, so perhaps a heuristic could be used to filter (and this may provide a cheap way to flag to users the problem word(s) in a simple manner)
As we have just announced today, in the coming weeks we will ask for help to improve how we extract valid sentences from large sources of data
Anything to improve our algorithms is more than welcomed
Cheers