
Hidden Markov Models (2014) [pdf] - alexeyr
https://web.stanford.edu/~jurafsky/slp3/8.pdf
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platz
\-- vs kalman filters:

"In both models, there's an unobserved state that changes over time according
to relatively simple rules, and you get indirect information about that state
every so often. In Kalman filters, you assume the unobserved state is
Gaussian-ish and it moves continuously according to linear-ish dynamics
(depending on which flavor of Kalman filter is being used). In HMMs, you
assume the hidden state is one of a few classes, and the movement among these
states uses a discrete Markov chain. In my experience, the algorithms are
often pretty different for these two cases, but the underlying idea is very
similar." \- THISISDAVE

\-- vs LSTM/RNN:

"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." -PRACCU

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

"IMO, anything that be done with an HMM can now be done with an RNN. The only
advantage that an HMM might have is that training it might be faster using
cheaper computational resources. But if you have the $$$ to get yourself a GPU
or two, this computational advantage disappears for HMMs." \- SHERJILOZAIR

~~~
nl
What/where/who are the people you are attributing these statements(?) to? They
seem fairly accurate, and I'm interested in seeing more.

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fitzwatermellow
Thanks for the link. Excellent text book for speech and language processing.

