  We present an asymptotic analysis of Viterbi Training (VT) and contrast it
with a more conventional Maximum Likelihood (ML) approach to parameter
estimation in Hidden Markov Models. While ML estimator works by (locally)
maximizing the likelihood of the observed data, VT seeks to maximize the
probability of the most likely hidden state sequence. We develop an analytical
framework based on a generating function formalism and illustrate it on an
exactly solvable model of HMM with one unambiguous symbol. For this particular
model the ML objective function is continuously degenerate. VT objective, in
contrast, is shown to have only finite degeneracy. Furthermore, VT converges
faster and results in sparser (simpler) models, thus realizing an automatic
Occam's razor for HMM learning. For more general scenario VT can be worse
compared to ML but still capable of correctly recovering most of the
parameters.
