Speech Recognition using Convolution Deep Neural Networks

The use of a speech recognition model has become extremely important. Speech control has become an important type; Our project worked on designing a word-tracking model by applying speech recognition features with deep convolutional neuro-learning. Six control words are used (start, stop, forward, backward, right, left). Words from people of different ages. Two equal parts, men and women, contribute to our speech dataset which is used to train and test proposed deep neural networks. Collect data in different places in the street, park, laboratory and market. Words ranged in length from 1 to 1.30 seconds for thirty people. Convolutional Neural Network (CNN) is applied as advanced deep neural networks to classify each word from our pooled data set as a multi-class classification task. The proposed deep neural network returned 97.06% as word classification accuracy with a completely unknown speech sample. CNN is used to train and test our data. Our work has been distinguished from many other papers that often use ready-made and fairly consistent data of the isolated word type. While our data are collected in different noisy environments under different conditions and from two types of speech, isolated word and continuous word.