Recurrent neural network with backpropagation through time for speech recognition

Abstract

Speech recognition and understanding have been studied for many years. The neural network is well-known as a technique that is able to classify nonlinear problems. Much research has been done in applying neural networks to solving the problem of recognizing speech such as Arabic. Arabic offers a number of challenges to speech recognition. We propose a fully-connected hidden layer between the input and state nodes and the output. We also investigate and show that this hidden layer makes the learning of complex classification tasks more efficient. We also investigate the difference between LPCC (linear predictive cepstrum coefficients) and MFCC (Mel-frequency cepstral coefficients) in the feature extraction process. The aim of the study was to observe the differences in the 29 letters of the Arabic alphabet from "alif" to "ya". The purpose of this research is to upgrade the knowledge and understanding of Arabic alphabet or words using a fully-connected recurrent neural network (FCRNN) and backpropagation through time (BPTT) learning algorithm. Six speakers (a mixture of male and female) in a quiet environment are used in training.