Abstract
Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation of basic linguistic units or phonemes, constructing words from phonemes and contextually analyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying the possibility of designing a software system using one of the techniques of artificial intelligence applications neuron networks where this system is able to distinguish the sound signals and neural networks of irregular users. Fixed weights are trained on those forms first and then the system gives the output match for each of these formats and high speed. The proposed neural network study is based on solutions of speech recognition tasks, detecting signals using angular modulation and detection of modulated techniques.
Highlights
Artificial intelligence applications have proliferated in recent years, especially in the applications of neural networks where they represent an appropriate tool to solve many problems highlighted by distinguished styles and classification
How to cite this paper: Al Smadi, T., Al Issa, H.A., Trad, E. and Al Smadi, K.A. (2015) Artificial Intelligence for Speech Recognition Based on Neural Networks
To highlight the activity of neural networks is the process of classification and coding and to highlight the properties of neural networks are: a) Resistance to noise; b) Flexibility in dealing with the distorted images; c) Maximum resistance to tag images of dismembered or partially decomposed; d) Combinations of parallel processes with a large number of operating units that stimulate by interdependence of processes in addition to the stock of information distributed in parallel
Summary
Artificial intelligence applications have proliferated in recent years, especially in the applications of neural networks where they represent an appropriate tool to solve many problems highlighted by distinguished styles and classification. Hip education Act initial contribution in neural network theory had been built and tested in the first study of the neurological computer in the 1950s, where the application contacts automatically and during this stage the term preceptor called the unit represented for neural cell to invent the term world and divorced on the neuron, he pioneered the term frank Rosenblatt in 1958. This invention was a viable training machine learning and classification of certain models by modulating communication components first. In the early 1960s, a new created method called Adaptive Linear Combiner developed a very useful law [2]
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