Development of integral model of speech recognition system for Uzbek language

In this paper investigates the approach to realization of recognition of Uzbek words on the basis of end-to-end models is considered. Also presented are some theoretical data on the architecture of neural networks used in the integrated model, and the results of preliminary experimental studies conducted on their basis. Deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long-range context that empowers RNNs. When trained end-to-end with suitable regularization, we find that deep BRNNs achieve a test set error of CER=49.1% on our dataset.