Intro to Hidden Markov Models (2010) [pdf]
-- vs kalman filters:<p>&quot;In both models, there&#x27;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.&quot; - THISISDAVE<p>-- vs LSTM&#x2F;RNN:<p>&quot;Some state-of-the-art industrial speech recognition [0] is transitioning from HMM-DNN systems to &quot;CTC&quot; (connectionist temporal classification), i.e., basically LSTMs. Kaldi is working on &quot;nnet3&quot; which moves to CTC, as well.
Speech was one of the places where HMMs were _huge_, so that&#x27;s kind of a big deal.&quot; -PRACCU<p>&quot;HMMs are only a small subset of generative models that offers quite little expressiveness in exchange for efficient learning and inference.&quot; - NEXTOS<p>&quot;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.&quot; - SHERJILOZAIR