07:47 pm, 10 Dec 06

### paper: a tutorial on hidden markov models

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?)

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?)

a prioridistributions with the other. Most intros show one way and practically imply that it is universal, causing much confusion. Modern speech recognition systems have several layers of HMM networks, often using both methods on different layers.evantrochee