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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

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? [0]: https://cmusphinx.github.io/ https://cmusphinx.github.io/

ftreml 7y ago
for me it was exactly that, yes. we had powerful hardware and budget available so there was no reason to stick to statistical models. the available benchmarks showed that kaldi easily outperforms cmusphinx - when starting from scratch you typically select the leader i would say
shakna 7y ago
I've avoided Sphinx after trying to use it, because: 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.
sgt101 7y ago
I think that sphinx is about as good as it can get. Without really significant technical change there is no prospect of the performance that would allow successful application to the kind of applications that people want to try.

ftreml 7y ago

for me it was exactly that, yes. we had powerful hardware and budget available so there was no reason to stick to statistical models. the available benchmarks showed that kaldi easily outperforms cmusphinx - when starting from scratch you typically select the leader i would say

shakna 7y ago

I've avoided Sphinx after trying to use it, because: 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.

sgt101 7y ago

I think that sphinx is about as good as it can get. Without really significant technical change there is no prospect of the performance that would allow successful application to the kind of applications that people want to try.