A Helium Speech Unscrambling Algorithm Based on Deep Learning

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%.

Keywords

• helium speech
• speech recognition
• deep learning
• corpus