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Due to the decrease in accuracy of speech recognition software in noisy environments, a robust enhancement method based on domain knowledge is proposed to ensure the safety of using speech control operations. Taking ship control as the application background, a domain knowledge graph is established for ship control. Ship control commands are extracted from nautical books and classic naval warfare film and television materials, and a Chinese speech dataset for ship control commands is constructed. A domain knowledge-embedded decoding method is proposed to correct the output control commands by calculating the matching degree between the recognition result and the domain knowledge graph. Experimental results show that compared with the current popular connection time sequence classification decoding method and attention mechanism-based decoding method, the proposed decoding method reduces the word error rate by 4.0% and 1.5% when recognizing noisy speech with a signal-tonoise ratio of 10dB and 20dB, respectively, and improves the accuracy of command recognition by 10.3% and 6.3%, respectively, improving the robustness of the speech recognition model in recognizing Chinese commands. [ABSTRACT FROM AUTHOR]

In a continuous speech recognition system, aiming at the complex environments (including the variability of speakers and environmental noise), the training data does not match the test data, which results in a low voice recognition rate. A speech recognition method based on adaptive deep neural network is studied. The improved regularized adaptive criterion and the adaptive deep neural network in the feature space are combined to improve data matching. The fusion of speaker identity vector i-vector and noise perception training are used to overcome speaker and environmental noise changes and improve the classification function of the output layer of the traditional deep neural network, which ensures the characteristics of compactness within the class and separation between classes. The test experiment was carried out by superimposing various background noises under the TIMIT English speech data set and the Microsoft Chinese speech data set. The results show that, compared with the current popular GMMHMM and traditional DNN speech acoustic models, our proposal decreases the recognition word error rate by 5.151% and 3.113% respectively, which improves the generalization performance and robustness of the model to a certain extent. [ABSTRACT FROM AUTHOR]