BLOCK-SYMMETRIC MODELS AND METHODS: NEW CLASS OF DISCRETE PROGRAMMING TASKS

A new class of tasks in setting and solving application-oriented tasks is developed and offered in this paper – block-symmetric models and methods of discrete programming. The general setting of block-symmetric tasks is demonstrated. It differs from the known tasks by the block properties, symmetry and existence of different types of variables. The algorithm of iterative imaging of polynomial computing complexity, allowing to solve the application-oriented tasks of high dimensionality, is developed for solving tasks of this class. The clustering task is stated as an example of setting of the application-oriented task of the discrete programming

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained