A Heliumspeech Unscrambling Method Based on Deep Learning with Phonetic Multi-Objective Optimization

Saturated diving plays an important role in fields such as navigation operations, ocean development, military oceanography, and maritime rescue and is indispensable for the marine economy. Heliumspeech communication is an essential component of deep-sea saturated diving operations and serves as the sole means of communication in such environments. This paper presents a heliumspeech unscrambling method for saturated diving based on phonetic multi-objective optimization using deep learning. The method consists of a heliumspeech correction network and a heliumspeech unscrambling network. First, a phonetic multi-objective optimization algorithm is used to design the correction network, which reduces the demand for large heliumspeech training datasets. Then, a saturated diving working language heliumspeech corpus is used to train the heliumspeech unscrambling network. Finally, the unscrambling network processes heliumspeech using a cognitive transfer learning algorithm. During unscrambling, the network continuously converts unscrambled heliumspeech into sample data and adds them to a supervised database, forming a closed-loop control system that dynamically adjusts the network parameters to adapt to variations in heliumspeech signals. This approach not only reduces the deep learning neural network’s reliance on large training datasets but also enhances unscrambling performance, particularly under dynamically changing diving depths. Simulation experiments demonstrate that the method effectively unscrambles heliumspeech with a low word error rate and fast convergence.