Accurate recognition of named entities from spoken instructions remains a significant challenge for automatic speech recognition (ASR) techniques in air traffic control (ATC), which limits the reliability of ASR-based applications. A promising solution to overcome this challenge is to integrate prior contextual knowledge into ASR since it contains rich named entities used in ATC communications. Although existing studies have investigated ATC-related contextual ASR techniques, there is a lack of benchmarks to evaluate the advantages of different approaches. In this article, a comprehensive comparative study is presented to explore effective contextual ASR approaches for the ATC domain. Specifically, several typical contextual ASR approaches are introduced in ATC to conduct a comprehensive comparison. Moreover, a novel contextual ASR model, denoted CATCNet, is presented to dedicatedly address the domain-specific problems in ATC, such as limited resources, fast speech, and volatile noise. Several evaluation metrics are proposed to validate the performance of comparison approaches based on the practical requirements of ATC efforts. Extensive experiments are conducted across two real-world ATC speech corpora to build the benchmark. The experimental results demonstrated that integrating context knowledge is effective in improving the recognition performance of named entities. Crucially, the proposed CATCNet outperforms other baseline models by confirming all technical improvements, achieving 80.0% and 86.54% instruction recognition accuracy (IRA) on the ATCSpeech and C-ATCSpeech corpora, respectively. It is believed that this work not only overcomes the bottleneck of ASR performance in the ATC domain, but also provides an applicable solution for ATC-related ASR applications.

Air Traffic Control (ATC) is a complicated, time-evolving, and real-time procedure to direct flight operations in a safer and ordered manner. Although enormous data storages are available during air traffic operations for over 40 years, data-driven intelligent application in aviation is still an emerging task due to the safety-critical issue. With the prevalence of the Next Generation ATC system, artificial intelligence (AI) -empowered research topics are attracting increasing attention from both industrial and academic domains and a high-quality dataset naturally becomes the prerequisite for such practices. However, almost all ATC-related datasets are only unimodal for certain tasks, which fails to comprehensively illustrate the traffic situation to further support real-world studies. To address this gap, a multimodal air traffic situation (M2ATS) dataset is constructed to advance AI-related research in the ATC domain, including airspace information, flight plan, trajectory, and speech. M2ATS covers 10362 flights ATC situation data, involving 110000+ utterances (104 hours) with diversity golden text annotations, 16 intents, and 51 slots. Considering the real-world ATC requirements, a total of 10 multimedia-related tasks (24 baselines) are designed to validate the proposed dataset, covering automatic speech recognition, natural language processing, and spatial-temporal data processing. New ATC-related metrics corresponding to ATC applications are proposed in addition to the common metrics to evaluate task performance. Extensive experiment results demonstrate that the selective baselines can achieve designed tasks on this new dataset, and further investigations are also required to address task and data specificities. It is believed that the proposed new dataset is a new practice to advance AI applications to an industrial scene, which not only promotes ATC-related applications but also provides diverse research topics in the common multimedia community.

Automatic spoken instruction understanding (SIU) of the controller-pilot conversations in the air traffic control (ATC) requires not only recognizing the words and semantics of the speech but also determining the role of the speaker. However, few of the published works on the automatic understanding systems in air traffic communication focus on speaker role identification (SRI). In this article, we formulate the SRI task of controller-pilot communication as a binary classification problem. Furthermore, the text-based, speech-based, and speech-and-text-based multi-modal methods are proposed to achieve a comprehensive comparison of the SRI task. To ablate the impacts of the comparative approaches, various advanced neural network architectures are applied to optimize the implementation of text-based and speech-based methods. Most importantly, a multi-modal speaker role identification network (MMSRINet) is designed to achieve the SRI task by considering both the speech and textual modality features. To aggregate modality features, the modal fusion module is proposed to fuse and squeeze acoustic and textual representations by modal attention mechanism and self-attention pooling layer, respectively. Finally, the comparative approaches are validated on the ATCSpeech corpus collected from a real-world ATC environment. The experimental results demonstrate that all the comparative approaches worked for the SRI task, and the proposed MMSRINet shows competitive performance and robustness compared with the other methods on both seen and unseen data, achieving 98.56% and 98.08% accuracy, respectively.

In this work, a deep learning-based framework is proposed to implement an autonomous pilot agent (APA), which serves as a human pseudo-pilot to assist air traffic controller (ATCO) training. A novel paradigm, including speech recognition, language understanding, pilot repetition generation (PRG), and text-to-speech (TTS), is designed to formulate the framework pipeline, which also incorporates a simulation system interface. We mainly focus on the PRG and TTS models to address the ATC specificities in this work. The neural architecture is proposed to generate the text repetition instruction by using a sequence-to-sequence text mapping. The Transformer block is improved to implement a high-efficient TTS model, in which the nonautoregressive mechanism is applied to achieve the parallel synthesis. A dedicated phoneme vocabulary is designed to cope with the multilingual issue in the ATC domain and address the out-of-vocabulary problem. With the APA framework, a virtual training mode is proposed to complete the training task without the limitation of time and location. Experimental results on a real-world dataset show that the proposed APA framework replaces the human pilot with considerable high confidence in a real-time manner during the simulation training. Most importantly, the APA framework and the virtual training system are able to cope with the dilemma of physical attendance (like COVID-19) and improve the equipment utilization capacity for the ATCO training.

This work focuses on robust speech recognition in air traffic control (ATC) by designing a novel processing paradigm to integrate multilingual speech recognition into a single framework using three cascaded modules: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The AM converts ATC speech into phoneme-based text sequences that the PM then translates into a word-based sequence, which is the ultimate goal of this research. The LM corrects both phoneme- and word-based errors in the decoding results. The AM, including the convolutional neural network (CNN) and recurrent neural network (RNN), considers the spatial and temporal dependences of the speech features and is trained by the connectionist temporal classification loss. To cope with radio transmission noise and diversity among speakers, a multiscale CNN architecture is proposed to fit the diverse data distributions and improve the performance. Phoneme-to-word translation is addressed via a proposed machine translation PM with an encoder-decoder architecture. RNN-based LMs are trained to consider the code-switching specificity of the ATC speech by building dependences with common words. We validate the proposed approach using large amounts of real Chinese and English ATC recordings and achieve a 3.95% label error rate on Chinese characters and English words, outperforming other popular approaches. The decoding efficiency is also comparable to that of the end-to-end model, and its generalizability is validated on several open corpora, making it suitable for real-time approaches to further support ATC applications, such as ATC prediction and safety checking.

Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR application. In this paper, a multilingual speech corpus (ATCSpeech) from real ATC systems, including accented Mandarin Chinese and English, is built and released to encourage the non-commercial ASR research in ATC domain. The corpus is detailly introduced from the perspective of data amount, speaker gender and role, speech quality and other attributions. In addition, the performance of our baseline ASR models is also reported. A community edition for our speech database can be applied and used under a special contrast. To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.

A deep learning-based safety monitoring framework for air traffic control (ATC) systems is proposed in this paper to reduce human errors and relieve the controllers' workload by regulating the controlling procedure, eliminating communication misunderstanding, monitoring flight conformance, and detecting potential conflicts. The framework comprises automatic speech recognition (ASR), controlling intent inference (CII), and control safety monitoring (CSM) subsystems. The pipeline of the proposed framework can be described as follows: the ASR subsystem translates the pilot-controller voice communications (PCVCs) into texts, which are then converted to the predefined data structure by the CII subsystem. Three types of air traffic safety measures, including repetition check, flight conformance verification, and potential conflict detection, are finally validated by the CSM subsystem. An improved end-to-end ASR model with convolutional, bidirectional long short-term memory (BLSTM) and fully connected (FC) layers is trained using the connectionist temporal classification loss function. The BLSTM and FC combined CII model is designed to infer the controlling intent and slot filling. A language model is also trained in this subsystem to improve the overall performance of the framework. After converting the PCVCs to ATC data, the CSM subsystem checks the given safety monitoring tasks and sends warnings to the current system. The experimental results show that the proposed ASR model obtains a better performance than that of other approaches, and the tasks in the CII subsystem are fulfilled with a high classification precision. The CSM subsystem is also tested to confirm its safety monitoring function by playing back the data and several simulated instructions. To the best of our knowledge, this is pioneering work in the safety monitoring of flight control by recognizing the PCVCs with deep learning-based methods.