Recurrent neural network with backpropagation through time for speech recognition

The study on speech recognition and understanding has been done for many years. In this paper, we propose a fully-connected hidden layer between the input and state nodes and the output. Besides that, we also investigate and show that this hidden layer makes the learning of complex classification tasks more efficient. We also investigate difference between LPCC and MFCC in feature extraction process. The aim of the study was to observe the difference of Arabic's alphabet like "alif" until "ya". The purpose of this research is to upgrade the people's knowledge and understanding on Arabic's alphabet or word by using Fully-Connected Recurrent Neural Network (FCRNN) and Backpropagation through Time (BPTT) learning algorithm. 6 speakers (a mixture of male and female) are trained in quiet environment. Neural Network is well-known as a technique that has the ability to classified nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition [1] such as Arabic. The Arabic language offers a number of challenges for speech recognition [2]. Even though positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots of attention. This research utilizes Recurrent Neural Network, one of Neural Network technique to observe the difference of alphabet "alif" until "ya".