The Development of Software Components for Streaming Audio Content Filtering Through the Use of Hidden Markov Models

The results of the development of efficient algorithms for streaming voice recognition using stochastic models based on the use of hidden Markov models are shown in this work. The article provides basic theoretical information for the hidden Markov model of the discrete system and the necessary parameters to define it are distinguished. Also there are three main tasks considered that need to be solved for the successful application of hidden Markov models in speech recognition systems. The algorithm of the method of Baum–Welch aimed at clarifying the parameters of the model and the Viterbi algorithm of selection of the most likely sequence
of states of the system are given. These two methods are implemented in the environment of graphical programming LabVIEW in the form of software modules that implement the construction of the hidden Markov models of individual words, using the method of Baum–Welch and recognition of these words on the basis of the Viterbi method. It is supposed to use these modules to implement streaming audio content filtering in digital communication systems.