Google Scholar reports that A tutorial on hidden Markov models and selected applications in speech recognition has 4661 citations -- and deservedly so. I've read presentations on what hidden Markov models are a bunch of times, but I hadn't quite grasped it until I read (and thought hard about) this paper. I eventually had a real aha! moment and it now (as many things do in CS) seems kinda obvious. The bulk of the paper is on speech recognition but I wouldn't expect a paper from the 80s to be too relevant to today's state of the art in that field, so I skipped it. But the math behind hidden Markov models doesn't change, so you can read the first nine pages and get quite a bit out of it.
Definitely recommended, though expect (at least if you're like me) to devote a bit of time to understanding it.
(I thought I posted about this earlier but I apparently haven't?)