Kaldi certainly is a research project. Hopefully in the same vein as Postgres and not so much a toy implementation.

Could you conceivably run DeepSpeech and Kaldi side-by-side on the same audio sample and gain any kind of accuracy benefit?

To start with, errors would have to be uncorrelated, and you would need confidence scores on each word, which I assume you get.

Deepspeech can have multiple models running at the same time so I obviously see a benefit from using another system. Kaldi however is much faster to run in my experience and deepspeech is rnn based which puts a limit on how much you can pararalize it.

There are ways to combine multiple independent speech recognition engines to improve accuracy, e.g. ROVER: http://my.fit.edu/~vkepuska/ece5527/sctk-2.3-rc1/doc/rover/r...

I haven't seen any setups like you describe, but if you want to build a framework for comparing confidence intervals that use case would be possible.

DeepSpeech is definitely not as accurate. You messed something up. Most companies doing speech use kaldi, including Amazon.

Please elaborate on this. How are they using it? What do they do to expand on it?