In this paper has been discussed about speech recognition using spectrogram images and deep convolution neural network(CNN) of Uzbek spoken digits. Spectrogram images from speech signal were generated and it were used for deep CNN training. Presented CNN model contains 3 convolution layers and 2 fully connected layers that discriminative features can be divided and estimated of spectrogram images by those layers. In current research period, dataset of Uzbek spoken digits were made and in based on presented CNN model they were trained. Testing results shows that, proposed approach for Uzbek spoken digits classified 100% accuracy.

The relevance of creating information systems using artificial intelligence methods and tools is dictated by the following reasons: The volume of scientific and educational information is growing; Traditional information retrieval methods have exhausted themselves. Using only deterministic and iteration methods, rigid algorithms don't give the expected results. They require more time to process information and more memory. Significant progress in recent years in the development of artificial intelligence (AI) methods and systems gives hope that their use will significantly reduce the time needed to search for data for scientific research and educational activities. The aim of the research results presented in the article is to increase the efficiency for scientific and educational information retrieval based on the use of AI methods implemented in the integrated intelligent information system “SMART TUIT". The article presents the results of theoretical and applied research obtained by several departments of the Tashkent University of Information Technology (TUIT) in solving the following tasks: Voice recognition for subsequent processing; Pattern recognition in order to identify the users of information; Search and processing of scientific and educational resources in electronic libraries; Analysis of information needs of users depending on the level of competence and type of activity; Evaluation of scientific and educational information to identify the most important data sources; Geoinformation system to solve the problems of the location of the information source. Initially, each research area in the departments was aimed at solving a certain class of problems related to medicine, linguistics, electronic libraries, corporate networks, information security systems, etc. The TUIT creative group decided to combine efforts to apply the results obtained to solve the important problem of intellectualizing the search for sources of scientific and educational information among a large amount of data.