Improvement of Speech Recognition Using Combination of Lexical Knowledge with Feature Parameters
Abstract
The high ability of humans in speech perception encourages the use of functional finding of brain in speech recognition. In this research, lexical models are created by using the inversion of neural networks and the NLPCA neural networks. These models can increase the phoneme correction rate up to 81%. We increase the correction rate by combining the lexical model with feature parameters. In this way, by two methods of neural network inversion, feature parameters can be improved, resulting in a correction rate of aboat 82.4%.
Keywords
speech recognition, NLPCA neural network, inversion of neural networks, bidirectional neural networks, lexical modeling
