Affiliation:
1. School of Information Engineering, Jiangsu College of Engineering and Technology, Nantong 226006, China
2. School of Information Science and Technology, Nantong University, Nantong 226019, China
Abstract
Helium speech, the language spoken by divers in the deep sea who breathe a high-pressure helium–oxygen mixture, is almost unintelligible. To accurately unscramble helium speech, a neural network based on deep learning is proposed. First, an isolated helium speech corpus and a continuous helium speech corpus in a normal atmosphere are constructed, and an algorithm to automatically generate label files is proposed. Then, a convolution neural network (CNN), connectionist temporal classification (CTC) and a transformer are combined into a speech recognition network. Finally, an optimization algorithm is proposed to improve the recognition of continuous helium speech, which combines depth-wise separable convolution (DSC), a gated linear unit (GLU) and a feedforward neural network (FNN). The experimental results show that the accuracy of the algorithm, upon combining the CNN, CTC and the transformer, is 91.38%, and the optimization algorithm improves the accuracy of continuous helium speech recognition by 9.26%.
Funder
National Natural Science Foundation of China
Nantong Science and Technology Project
Natural Science and Technology Project of Jiangsu Engineering Vocational and Technical College
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