Approaches to Solving Problems of Markov Modeling Training in Speech Recognition

The article discusses approaches to solving problems of learning Markov modeling in speech recognition. A Markov process is a stochastic process consisting of a sequence of random states, where the probability of transition from one state to another depends only on the current state and does not depend on previous states. The result of observing such a process is a sequence of states that the system goes through during the observation period. The task of model training is considered the most difficult when using Markov models in recognition systems, since there is no known unique and universal way to solve it, and the quality of recognition depends on the result of model training. Therefore, special attention must be paid to training the model.