Offline Automatic Speech Recognition System Based on Bidirectional Gated Recurrent Unit (Bi-GRU) with Convolution Neural Network

Abstract

In recent years, the usage of smart phones increased rapidly. Such smartphones can be controlled by natural human speech signals with the help of automatic speech recognition (ASR). Since a smartphone is a small gadget, it has various limitations like computational power, battery, and storage. But the performance of the ASR system can be increased only when it is in online mode since it needs to work from the remote server. The ASR system can also work in offline mode, but the performance and accuracy are less when compared with online ASR. To overcome the issues that occur in the offline ASR system, we proposed a model that combines the bidirectional gated recurrent unit (Bi-GRU) with convolution neural network (CNN). This model contains one layer of CNN and two layers of gated Bi-GRU. CNN has the potential to learn local features. Similarly, Bi-GRU has expertise in handling long-term dependency. The capacity of the proposed model is higher when compared with traditional CNN. The proposed model achieved nearly 5.8% higher accuracy when compared with the previous state-of-the-art methods.