Research on a Deep Learning Method for Speech Recognition

Deep convolutional neural network (CNN) has been widely used in speech recognition technology. The model based on deep CNN can effectively improve the quality of human-computer interaction. However, the existing CNN with fixed convolutional kernel size has a disadvantage on extracting data features. It is hard to effectively identify whether the extracted features sufficient or not. As a result, a self-tuning convolutional kernel (STCK) algorithm is proposed to solve the mentioned problem. Firstly, the computational process of STCK algorithm is derived. Then the calculation formula of the convolutional kernel size is obtained. Meanwhile, Bark-spectrum is introduced to extract the spectrogram of speech signal, which is used as the CNN input to adapt to the human hearing. In addition, the data enhancement strategies are proposed, namely frame channel shielding and Bark-band channel shielding. The presented strategies can further improve the generalization ability of the recognition model. The experimental results show that, compared with another two models (the CNN model without STCK algorithm and the CNN model without the data enhancement strategy), the training loss of the proposed method is minimum. And the recognition error rates for the test samples are reduced by 3.9% and 1%, respectively. © (2024), (International Association of Engineers). All Rights Reserved.