A Hybrid Speech Recognition System Using Deep Learning Methods

Speech-to-text Conversion is a type of Speech Recognition Program that effectively takes audio content as input and transcribes it into written words. With increasing technologies and large data corpus, the importance of speech recognition has increased. Now everyone seems to be exploitation Speech Recognition Technology for users to work a tool, perform commands, or write while not having to use a keyboard, mouse, or press any buttons. It is also easy for everyone to utter sound or speak than using hands to be work done and it is also convenient to use. In this paper, a system capable of converting audio files to text has been developed. The proposed system consists of a set of algorithms for processing audio files, where the MFCC algorithm combine with standard deviation was adopted to extract the features of the audio file and convert it into an image. The features of audio files are stored as images because deep learning algorithms can be trained on images better than CSV files. The second part of the proposed system is the design of a deep learning model in which two algorithms, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are combined to predict words. The model consists of a set of layers to extract the features from the images, choose the best features, then train and classify them based on the proposed DNN model. In this thesis, three types of datasets (Arabic, English, and Real) were adopted to test the proposed system in speech prediction and the accuracy of the proposed system has reached more than 95%.