# What are some applications of unsupervised HMMs?

Supervised HMMs can be applied to many problems like POS tagging and OCR (optical character recognition).

I've learned that HMMs can be trained unsupervisedly using EM (Baum-Welch algorithm), what are some example applications of this unsupervised approach?

In general, suppose you have a given set of observed sequences $\mathcal{O}= \{x_1, x_2,...x_n\}$ (could be speech recordings, sequences nucleotides, amino acids ... etc) and you are interested in analyzing the sequence based on your Hidden Markov Model. However, the solution to your analysis is meaningful only if the HMM can properly model the sequence of interest. Thus the important question is the reasonable choice for HMM parameters for the given sequence. This is where Baum-Welch algorithm is useful. Although there is no ideal way to estimate the parameters from a limited number of observed sequences, Baum-Welch algorithm lets you do it in a locally optimal way. The same can be accomplished using the segmental k-means algorithm (which is an extension of Viterbi algorithm) which differs from Baum-Welch algorithm in the optimization step.