Yes, I've used sphinx, not quite recently, and when I last used it (which was years ago), it was trivial to set up a working english install with the pre-provided models, as an amateur. From there it's only the problem of improving the model (which is obviously a pretty hard problem).
Are you saying that the sphinx progress is at that same level currently as the state of the art in the 90s? Surely that's hyperbole.
Also, using AI to generate even better models doesn't seem like a bad idea, if you're looking to improve it -- the fundamentals of speech recognition haven't changed, why build a completely separate open source product instead of contributing to something already well understood and accessible?
As far as I can tell, CMU Sphinx is still based on HMMs, which were the previous state of the art before neural networks brought a breakthrough in model performance. So it is likely that CMU Sphinx is currently not much better than what was possible in the 90s. When I last looked into this, I found a mailing-list message by one of the maintainers, where he explicitly recommended to use Kaldi if you want better results.
Kaldi does support neural network models in addition to good old HMMs, but it is very "researchy": everything is set up so that you can replace any step in the pipeline by your components (and then publish a paper about your results). But that also means that you pretty much have to be an expert to correctly assemble a working product from the available components, and it's pretty much assumed that you will be training your own models, which can be difficult when you don't have access to lots of data.
So yes, the existing landscape for open-source speech recognition leaves something to be desired, and the focus of existing projects doesn't necessarily lend itself to turning them into what you want.
Sorry, I wasn't clear -- what I meant to say is that even inside the limitations of HMM, it's absurd to imply that CMUSphinx has made no progress on the state of the art in the 90s. A greatly improved methodology/approach to HMM is hard won, and it's unfair to minimize that effort that produced progreess in an old method just because a new method has been developed.
neural network models have their own downsides, the biggest one of which being training -- why throw the baby out with the bathwater instead of taking the approach kaldi has taken, possibly building a neural network model alternative inside sphinx?
Let CMU plug away at making HMMs better, while you plug away at making neural networks better, but interoperate so everyone gets both benefits? You can even maybe make some headway with the neural network bootstrapping problem with some help from the progress CMU has already made.
Are you saying that the sphinx progress is at that same level currently as the state of the art in the 90s? Surely that's hyperbole.
Also, using AI to generate even better models doesn't seem like a bad idea, if you're looking to improve it -- the fundamentals of speech recognition haven't changed, why build a completely separate open source product instead of contributing to something already well understood and accessible?