Persian speech recognition using deep learning

Up to now, various methods are used for Automatic Speech Recognition (ASR), and among which the Hidden Markov Model (HMM) and Artificial Neural Networks (ANNs) are the most important ones. One of the existing challenges is increasing the accuracy and efficiency of these systems. One way to enhance the accuracy of them is by improving the acoustic model (AM). In this paper, for the first time, the combination of deep belief network (DBN), for extracting features of speech signals, and Deep Bidirectional Long Short-Term Memory (DBLSTM) with Connectionist Temporal Classification (CTC) output layer is used to create an AM on the Farsdat Persian speech data set. The obtained results show that the use of a deep neural network (DNN) compared to a shallow network improves the results. Also, using the bidirectional network increases the accuracy of the model in comparison with the unidirectional network, in both deep and shallow networks. Comparing obtained results with the HMM and Kaldi-DNN indicates that using DBLSTM with features extracted from the DBN increases the accuracy of Persian phoneme recognition.