DEVELOPMENT OF THE NEURAL NETWORK FOR SOLVING THE PROBLEM OF SPEECH RECOGNITION
Ключевые слова:
speech recognition, neural networks, error back propagation algorithm, learning, learning rate.Аннотация
The article discusses a method for solving the problem of speech recognition on the example of
recognizing individual words of a limited dictionary using a forward propagation neural network trained by the error
back propagation method. The goal was to create a neural network model for recognizing the solution of individual
words, analyze the training characteristics and behavior of the constructed neural network. Based on the input data
and output requirements, a feedback neural network selected. To train the selected neural network model, a back
propagation algorithm was chosen. The developed neural network demonstrated the expected behavior associated
with learning and generalization errors. It found that even if the generalization error decreases as the learning
sequence increases, the errors begin to fluctuate regardless of the introduction of a dynamic learning rate. The
network sufficiently trained to meet the generalization error requirements, but there is stillroom to improve the
generalization error. Practical results of training the constructed neural network at different sizes of the training
sample presented.
