  This research focuses on the algorithms and approaches for learning Hidden
Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a
statistical Markov model in which the system being modeled is assumed to be a
Markov process. One of the essential characteristics of HMMs is their learning
capabilities. Learning algorithms are introduced to overcome this
inconvenience. One of the main problems of the newly proposed algorithms is
their validation. This research aims by using the theoretical and experimental
analysis to 1) compare HMMs learning algorithms proposed in the literature, 2)
provide a validation tool for new HMM learning algorithms, and 3) present a new
algorithm called Asexual Reproduction Optimization (ARO) with one of its
extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the
validation tool proposed. According to the literature findings, it seems that
populationbased algorithms perform better among HMMs learning approaches than
other algorithms. Also, the testing was done in nine benchmark datasets. The
results show that MARO outperforms different algorithms in objective functions
in terms of accuracy and robustness.
