Lexicon learning-based heliumspeech unscrambling method in saturation diving

A lexicon learning-based heliumspeech unscrambling method in saturation diving, applicable to a system comprising at least one diver i, one heliumspeech correction network, and one heliumspeech unscrambling network, wherein a heliumspeech signal of the diver i is S, and the heliumspeech unscrambling method comprises the following steps:

• step 1. lexicon signal construction-constructing a common working language lexicon K of the diver i for saturation diving operation according to saturation diving specifications;
• step 2. supervision signal generation-reading, by the diver i, words in the lexicon K in a normal atmospheric environment to obtain supervision signals Xi, to generate a supervision signal set X={Xi} of the heliumspeech correction network for machine learning, wherein i=1, 2, . . . , I, and I is a number of divers;
• step 3. vector signal generation-reading, by the diver i, the words in the lexicon K respectively in environments corresponding to saturation diving depths h1, h2, h3, . . . , hL, to obtain vector signals Yi,l, wherein l=1, 2, . . . , L, and L is a number of heliumspeech test points, to generate a vector signal set Y={Yi,l} of the heliumspeech correction network for machine learning;
• step 4. learning of the heliumspeech correction network-performing, by the heliumspeech correction network, supervised learning by using the vector signals Yi,l as input signals and the supervision signals Xi as expected output signals to form a correction network parameter set C={Ci,l} corresponding to the vector signals Yi,l;
• step 5. correction network parameter selection-fitting the heliumspeech signal S of the diver i during saturation diving operation with all the vector signals Yi,l in the vector signal set Y, and selecting a parameter Cn,l corresponding to a vector signal Yn,l having a highest fitness as a correction network parameter;
• step 6. heliumspeech correction-correcting the heliumspeech signal S by using the heliumspeech signal S as an input signal of the heliumspeech correction network, to generate a corrected speech signal T;
• step 7. learning of the unscrambling network-comparing speeches in the corrected speech signal T with speeches in the supervision signals in the supervision signal set X of the heliumspeech correction network for machine learning word by word, to calculate fitnesses therebetween;
• selecting, from the supervision signal set X, speeches corresponding to words having the highest fitnesses;
• matching the selected speeches with speeches corresponding to the words in the corrected speech signal T into groups;
• sorting, in descending order by fitness, the matched speeches of the groups;
• selecting a top p % of the groups in orders of the fitness;
• taking speeches in the corrected speech signal T in the selected top p % of the groups as a vector signal U of the unscrambling network for machine learning and taking speeches corresponding to words in the supervision signal set X in the selected top p % of the groups as a supervision signal V of the heliumspeech unscrambling network for machine learning, and
• performing, by the unscrambling network, supervised learning;
• and
• step 8. heliumspeech unscrambling-unscrambling the heliumspeech signal S by using the corrected speech signal T as an input signal of the heliumspeech unscrambling network.