Abstract
Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.