Hidden Markov Models for Pattern Recognition

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Hidden Markov Models (HMMs) are the most popular recognition algorithm for pattern recognition. Hidden Markov Models are mathematical representations of the stochastic process, which produces a series of observations based on previously stored data. The statistical approach in HMMs has many benefits, including a robust mathematical foundation, potent learning and decoding techniques, effective sequence handling abilities, and flexible topology for syntax and statistical phonology. The drawbacks stem from the poor model discrimination and irrational assumptions required to build the HMMs theory, specifically the independence of the subsequent feature frames (i.e., input vectors) and the first-order Markov process. The developed algorithms in the HMM-based statistical framework are robust and effective in real-time scenarios. Furthermore, Hidden Markov Models are frequently used in real-world applications to implement gesture recognition and comprehension systems. Every state of the model can only observe one symbol in the Markov chain. In contrast, every state in the topology of a Hidden Markov Model can see one symbol emerging from a particular gesture. The matrix representing the observation probability distribution contains the likelihood of observing a symbol in each state. As an illustration, the probability that a symbol will emit is determined by its observation probability in the first state. In the recognition task, the emission distribution is another name for the observation probability distribution. For the following reasons, HMM states are also referred to as hidden. First, choosing to emit a symbol denotes the second process. Second, an HMM’s emitter only releases the observed symbol. Finally, since the current states are derived from the previous states, the emitting states are unknown. HMMs are well-known and more flexible in the field of gesture recognition because of their stochastic nature.