hmm

HMMs are the classic probabilistic sequence model behind speech and tagging, and Viterbi is how you decode them. The signal is the hidden-states plus transitions/emissions structure, and that Viterbi is dynamic programming for the single best state path, not a probability.

An HMM is generative and assumes independent emissions. A CRF is discriminative, so it takes arbitrary overlapping features. Both decode with Viterbi.