The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study, a new E2E framework, ResNet–GAU–CTC, is proposed to implement Mandarin speech recognition for air traffic control (ATC). A deep residual network (ResNet) utilizes the translation invariance and local correlation of a convolutional neural network (CNN) to extract the time-frequency domain information of speech signals. A gated attention unit (GAU) utilizes a gated single-head attention mechanism to better capture the long-range dependencies of sequences, thus attaining a larger receptive field and contextual information, as well as a faster training convergence rate. The connectionist temporal classification (CTC) criterion eliminates the need for forced frame-level alignments. To address the problems of scarce data resources and unique pronunciation norms and contexts in the ATC field, transfer learning and data augmentation techniques were applied to enhance the robustness of the network and improve the generalization ability of the model. The character error rate (CER) of our model was 11.1% on the expanded Aishell corpus, and it decreased to 8.0% on the ATC corpus.
Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.
The fatiguing work of air traffic controllers inevitably threatens air traffic safety. Determining whether eyes are in an open or closed state is currently the main method for detecting fatigue in air traffic controllers. Here, an eye state recognition model based on deep-fusion neural networks is proposed for determination of the fatigue state of controllers. This method uses transfer learning strategies to pre-train deep neural networks and deep convolutional neural networks and performs network fusion at the decision-making layer. The fused network demonstrated an improved ability to classify the target domain dataset. First, a deep-cascaded neural network algorithm was used to realize face detection and eye positioning. Second, according to the eye selection mechanism, the pictures of the eyes to be tested were cropped and passed into the deep-fusion neural network to determine the eye state. Finally, the PERCLOS indicator was combined to detect the fatigue state of the controller. On the ZJU, CEW and ATCE datasets, the accuracy, F1 score and AUC values of different networks were compared, and, on the ZJU and CEW datasets, the recognition accuracy and AUC values among different methods were evaluated based on a comparative experiment. The experimental results show that the deep-fusion neural network model demonstrated better performance than the other assessed network models. When applied to the controller eye dataset, the recognition accuracy was 98.44%, and the recognition accuracy for the test video was 97.30%.
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