I don't think that's true.
That is a big factor in the accuracy of speech-to-text engines, but I don't think training data is a problem for text-to-speech (which is what the OP was complaining about).
That is a big factor in the accuracy of speech-to-text engines, but I don't think training data is a problem for text-to-speech (which is what the OP was complaining about).
It is very, very difficult to find a large, well curated dataset of speech with accompanying text labels. TIMIT is the gold standard of speech recognition despite its simplicity, and it costs ~$150(!) to get access at all. Switchboard is another famous one which is bigger, but only partially labeled and still very hard to get. CMU has an openly available, small dataset called AN4, but it is only 64MB of raw recordings (a lot for 1991 when it was recorded, but still small today).
You could use YouTube and their automated translations, but the text is shoddy at best and definitely too unreliable to feed to an algorithm without significant cleansing.
Honestly, U.S. Congressional speeches may be the best "open" option IMO - good transcriptions, a variety of speakers, and TONS of data. If you could sync up the transcriptions with recordings of C-SPAN it could work very well, though I have not seen a dataset that puts this together. This still only covers English!
Companies understand that data is valuable - that is why they take the rights to it in every EULA! Well curated internal datasets will be the KFC/Coke secret recipes of the "big data" age.
I don't know if they have any plan to open their data but if someone could find a way to increase the amount of voice donors while improving an open dataset, it would be interesting. Just a thought, they are most certainly many more issues / options in this field.
[1]http://www.ted.com/talks/rupal_patel_synthetic_voices_as_uni...