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Some state-of-the-art industrial speech recognition [0] is transitioning from HMM-DNN systems to "CTC" (connectionist temporal classification), i.e., basically LSTMs. Kaldi is working on "nnet3" which moves to CTC, as well.

Speech was one of the places where HMMs were _huge_, so that's kind of a big deal.

[0] http://googleresearch.blogspot.com/2015/09/google-voice-sear...

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I'm kind of more interested in if HMM's are more viable as a general-purpose tools rather than their applicability in cutting-edge research; or if they should generally be avoided for common tasks unless the domain is simple enough.

I wonder how much of ANN popularity is due to the inherent strength of the model, and how much of it is due to large corporations funding/hiring people who happen to be interested in that particular flavor of machine learning.

HMMs are only a small subset of generative models that offers quite little expressiveness in exchange for efficient learning and inference.

Thanks

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