Can anyone in the space expand on why it's increasingly rare to see people using/building on Sphinx[0]? Do people avoid it simply because of an impression that it won't be good enough compared to deep learning driven approaches?
1. Compiling it is hit and miss. Sometimes it works, sometimes it doesn't. There is no official package in any Linux distribution, so packaging anything with it is incredibly painful. There's no easy way to cross-compile your project, so you'll end up working around the build process.
2. The documentation is woeful.
> Recent CMUSphinx code has noise cancellation featur. In sphinxbase/pocketsphinx/sphinxtrain it’s ‘remove_noise’ option. In sphinx4 it’s Denoise frontend component. So if you are using latest version you should be robust to noise in some degree already. Most modern models are trained with noise cancellation already, if you have your own model you need to retrain it.
> The algorithm impelmented is spectral subtraction in on mel filterbank. There are more advanced algorithms for sure, if needed you can extend the current implementation.
Inconsistent methods across the codebases prevents you knowing where to look, and if it is documented, it may involve spelling errors which you have to guess around (like above). Also plenty of vague references to other documents that may or may not even exist.