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Open source Kaldi gives you 7.8%, Microsoft didn't went too far.

Also, major issue with this kind of research is that they combined several systems in order to get best results. Most practical systems don't use combinations, they are too slow.




So this model won't be free software??? Odd, and bummer...

Also I'm note sure an error reduction of 20% (1-6.3/7.8) is to be considered small; depends on the particular challenge really. Like, sentiment analysis only starts to get interesting above 80% on some dataset, as much can be guessed correctly in very naive ways..

Human lvl on this task is estimated to be ~4% so we have quite a lot of ground to cover still..


There are 33% less errors with the Microsoft solution then with Kaldi... one could say that is quite significant.


Relative decrease in WER is not so significant for lower percentages. How about "we make 6 errors on 100 words but Kaldi makes 7".


It is cool anyone can use CNTK to produce something similar now


23% only


Maybe they can distillate the ensemble in a compact and efficient version for production.


Could you please provide a link?





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