Analyzing the Models of Speech Recognition on the Basis of Neural Networks of Deep Learning for Examination of Digital Phonograms

The authors analyze the models based on deep learning neural networks on the basis of the general approach to pauses and speech signals as different types of audio information fixed in a phonogram, different in some characteristics. It is shown that such an approach allows generating the learning database with the use of the general for pauses and signals of speech methods of preliminary processing of information. This provides a higher level of unification of network learning methods intended for solution of various examination problems.