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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 secret recipes of the "big data" age. beenpoor 142 days ago linkI can upvote this enough. There are very few companies that are masters of this game. Because it is extremely difficult to build. Every stage is a painful process and there are so many moving parts. To begin with you need very high quality recordings. Successful companies have voice directors whose job is to record voice at most optimal settings (and to make sure speaker speaks is neutral dispassionate tone). And then there is the manual process of segmentation and phoneme alignment That requires lot of human effort. Then you run the code! It will take quite sometime. Then you study the quality of synthesis. Fix issues manually (for example, if you are using concatenative synthesis, you may have to fix offending phoneme alignment based on feedback). And iterate on this. They work to provide people having speech impairments with unique synthetic voices thanks to voice donors. Just a thought, they are most certainly many more issues options in this field.[1]garrettdreyfus 141 days ago linkfinally a use for the filibuster . tomarr Nike Air Max Thea Green Leather 142 days ago linkI wonder if the audio data from television programmes combined with subtitles would Nike Air Max 2015 All White

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It a shame that the quality of open source text to speech engines is so much worse than the current commercial state of the art, as that the most notable difference in the demo videos between this and something like Siri. Would fixing that just be a matter of recording more high quality free sample libraries, etc., or are there fundamental technical challenges to solve? ar7hur 142 days ago linkIt mostly a question of training data. Google trains its acoustic models on thousands of hours of annotated audio samples. Most of the fancy algorithms for doing speech recognition are on github. What isn is a massive and diverse dataset. 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. 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 Mens Nike Air Max 2015 Running Shoes Amazon

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make a good training set. Here in the UK many of the subtitles are very good quality (presumably human transcribed), and would be easy to strip out non language noises such as laughter (normally transcribed in brackets). toomuchtodo 142 days ago linkGoogle used the GOOG 411 automated directory assistance number to collect data for training. welterde 142 days ago link0xdeadbeefbabe 142 days ago linkEconomics is the main challenge because it a difficult task and the demand for it is low (market is small if demanding [0]). But the mbrola people, who have a nice free speech synthesis program that also isn opensource, [1] say it a hard technical problem too.[0] http:

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