The exponential advances in artificial intelligence (AI) and machine learning (ML) have opened the door of automation to many applications. Examples are automatic speech recognition (ASR) [1
] applied to personal assistants (e.g., SIRI®
or Amazon’s ALEXA®
) and natural language processing (NLP) and understating [2
] for different tasks such as sentiment analysis [3
] and user intent detection [4
]. Even though these advances are remarkable, many applications have lagged behind due to their critical matter, imperative near-to-perfect performance or simply because the users or administrators only trust the already existing legacy systems. One clear example is air traffic control (ATC) communications.
In ATC communications, ATCos are required to issue verbal commands to pilots in order to keep control and safety of a given area of airspace, although there are different means of communication, such as controller–pilot data link communications (CPDLC). CPDLC is a two-way data link system by which controllers can transmit non-urgent strategic messages to an aircraft as an alternative to voice communications. These messages are displayed on a flight deck visual display.
Research targeted at understanding spoken ATC communications in the military domain can be traced back to the 1970s [5
], late 1980s [6
], and 1990s [7
]. Recent projects are aiming at integrating AI-based tools into ATC processes by developing robust acoustic-based AI systems for transcribing dialogues. For instance, MALORCA [8
], HAAWAAI [10
] and ATCO2 [11
]. These latest projects have shown mature-enough ASR and NLP systems that demonstrate potential for deployment in real-life operation control rooms. Other fields of work are voice activity detection (VAD), diarization [13
] and ASR [14
]. In addition, a few researchers have gone further by developing techniques to understand the ATCo–pilot dialogues [9
]. However, previous works are mostly disentangled from each other. Some researchers only focus on ASR [18
], while a few prior studies have integrated natural language understanding into their ASR pipelines [14
Another key application that has seen growth in interest is the ATCo training framework. Training ATCos usually involves a human simulation-pilot. The simulation-pilot responds to or issues a request to the ATCo trainee in order to simulate an ATC communication with standard phraseology [21
]. It is a human-intensive task, where a specialized workforce is needed during ATCo training and the overall cost is usually high. An example is the EUROCONTROL’s ESCAPE lite simulator https://www.eurocontrol.int/simulator/escape
, accessed on 12 May 2023) which still requires a human simulation-pilot. In a standard training scenario, the default simulation-pilots (humans) are required to execute the steps given by ATCo trainees, as in the case of real pilots (directly introduced to the simulator). The pilots, on the other hand, update the training simulator, so that the ATCos can see whether the pilots are following the desired orders. Therefore, this simulation is very close to a real ATCo–pilot communication. One well-known tool for ATCo training is Eurocontrol’s ESCAPE simulator. It is an air traffic management (ATM) real-time simulation platform that supports: (i) airspace design for en-route and terminal maneuvering areas; (ii) the evaluation of new operational concepts and ATCo tools; (iii) pre-operational validation trials; and most importantly, (ii) the training of ATCos [22
]. In this paper, we develop a virtual simulation-pilot engine that understands ATCo trainees’ commands and possibly can replace current simulators based on human simulation-pilots. In practice, the proposed virtual simulation-pilot can handle simple ATC communications, e.g., the first phase of the ATCo trainee’s training. Thus, humans are still required for more complex scenarios. Analogous efforts of developing a virtual simulation-pilot agent (or parts of it) have been covered in [23
In this paper, we continue our previous work presented at SESAR Innovation Days 2022 [25
]. There, a simple yet efficient ‘proof-of-concept’ virtual simulation-pilot was introduced. This paper formalizes the system with additional ATM-related modules. It also demonstrates that open-source AI-based models are a good fit for the ATC domain. Figure 1
contrasts the proposed pipeline (left side) and the current (default) human-based simulation-pilot (right side) approaches for ATCo training.
Main contributions. Our work proposes a novel virtual simulation-pilot system based on fine-tuning several open-source AI models with ATC data. Our mains contributions are:
Could human simulation pilots be replaced (or aided) by an autonomous AI-based system? This paper presents an end-to-end pipeline that utilizes a virtual simulation-pilot capable of replacing human simulation-pilots. Implementing this pipeline can speed up the training process of ATCos while decreasing the overall training costs.
Is the proposed virtual simulation-pilot engine flexible enough to handle multiple ATC scenarios? The virtual simulation-pilot system is modular, allowing for a wide range of domain-specific contextual data to be incorporated, such as real-time air surveillance data, runway numbers, or sectors from the given training exercise. This flexibility boosts the system performance, while making its adaptation easier to various simulation scenarios, including different airports.
Are open-source AI-based tools enough to develop a virtual simulation-pilot system? Our pipeline is built entirely on open-source and state-of-the-art pre-trained AI models that have been fine-tuned on the ATC domain. The Wav2Vec 2.0 and XLSR [26
] models are used for ASR, BERT [28
] is employed for natural language understanding (NLU), and FastSpeech2 [29
] is used for the text-to-speech (TTS) module. To the best of our knowledge, this is the first study that utilizes open-source ATC resources exclusively [11
Which scenarios can a virtual simulation-pilot handle? The virtual simulation-pilot engine is highly versatile and can be customized to suit any potential use case. For example, the system can employ either a male or a female voice or simulate very high-frequency noise to mimic real-life ATCo–pilot dialogues. Additionally, new rules for NLP and ATC understanding can be integrated based on the target application, such as approach or tower control.
The authors believe this research is a game changer in the ATM community due to two aspects. First, a novel modular system that can be adjusted to specific scenarios, e.g., aerodrome control or area control center. Second, it is demonstrated that open-source models such as XLSR [27
] (for ASR) or BERT [28
] (for NLP and ATC understanding) can be successfully adapted to the ATC scenario. In practice, the proposed virtual simulation-pilot engine could become the starting point to develop more inclusive and mature systems aimed at ATCo training.
The rest of the paper is organized as follows. Section 2
describes the virtual simulation-pilot system, covering the fundamental background for each of the base (Section 2.1
) and optional modules (Section 2.2
) of the system. Section 3
describes the databases used. Then, Section 4
covers the experimental setup followed for adapting the virtual simulation-pilot and the results for each module of the system. Finally, brief future research directions are provided in Section 5
and the paper is concluded in Section 7
2. Virtual Simulation-Pilot System
The virtual simulation-pilot system manages the most commonly used commands in ATC. It is particularly well-suited for the early stages of ATCo training. Its modular design allows the addition of more advanced rules and grammar to enhance the system’s robustness. Our goal is to enhance the foundational knowledge and skills of ATCo trainees. Furthermore, the system can be customized to specific conditions or training scenarios, such as when the spoken language has a heavy accent (e.g., the case of foreign English) or when the ATCo trainee is practicing different positions.
In general, ATC communications play a critical role in ensuring the safe and efficient operation of aircraft. These communications are primarily led by ATCos, who are responsible for issuing commands and instructions to pilots in real-time. The training process of ATCos involves three stages: (i) initial, (ii) operational, and (iii) continuation training. The volume and complexity of these communications can vary greatly depending on factors such as the airspace conditions and seasonal fluctuations, with ATCos often facing increased workloads during peak travel seasons [25
]. As such, ATCo trainees must be prepared to handle high-stress and complex airspace situations through a combination of intensive training and simulation exercises with human simulation-pilots [33
]. In addition to mastering the technical aspects of air traffic control, ATCo trainees must also develop strong communication skills, as they are responsible for ensuring clear and precise communication with pilots at all times.
Due to the crucial aspect of ATC, efforts have been made to develop simulation interfaces for their training [33
]. Previous works includes optimization of the training process [36
], post-evaluation of each training scenario [37
], and virtual simulation-pilot implementation, for example, a deep learning (DL)-based implementation [39
]. In [24
], the authors use sequence-to-sequence DL models to map from spoken ATC communications to high-level ATC entities. They use the well-known Transformer architecture [40
]. Transformer is the base of the recent, well-known encoder–decoder models for ASR (Wav2Vec 2.0 [26
]) and NLP (BERT [28
]). The subsections address in more detail each module that is a part of the virtual simulation-pilot system.
2.1. Base Modules
The proposed virtual simulation-pilot system (see Figure 1
) is built with a set of base modules, and possibly, optional submodules. The most simple version of the engine contains only the base modules.
2.1.1. Automatic Speech Recognition
Automatic speech recognition (ASR) or speech-to-text systems convert speech to text. An ASR system uses an acoustic model (AM) and a language model (LM). The AM represents the relationship between a speech signal and phonemes/linguistic units that make up speech and is trained using speech recordings along with their corresponding text. The LM provides a probability distribution over a sequence of words, provides context to distinguish between words and phrases that sound similar and is trained using a large corpus of text data. A decoding graph is built as a weighted finite state transducer (WFST) [41
] using the AM and LM that generates text output given an observation sequence. Standard ASR systems rely on a lexicon, LM and AM, as stated above. Currently, there are two main ASR paradigms, where different strategies, architectures and procedures are employed for blending all these modules in one system. The first is hybrid-based ASR, while the second is a more recent approach, termed end-to-end ASR. A comparison of both is shown in Figure 2
Hybrid-Based Automatic Speech Recognition.
ASR with hybrid systems is based on hidden Markov models (HMM) and deep neural networks (DNN) [44
]. DNNs are an effective module for estimating the posterior probability of a given set of possible outputs (e.g., phone-state or tri-phone-state probability estimator in ASR systems). These posterior probabilities can be seen as pseudo-likelihoods or “scale likelihoods”, which can be interfaced with HMM modules. HMMs provide a structure for mapping a temporal sequence of acoustic features, X
, e.g., Mel-frequency cepstral coefficients (MFCCs), into a sequence of states [45
]. Hybrid systems remain one of the best approaches for building ASR engines based on lattice-free maximum mutual information (LF-MMI) [46
]. Currently, HMM-DNN-based ASR is the state-of-the-art system for ASR in ATC domain [15
Recent work in ASR has targeted different areas in ATC. For instance, a benchmark for ASR on ATC communications databases is established in [47
]. Leveraging non-transcribed ATC audio data using semi-supervised learning has been covered in [48
] and using self-supervised learning for ATC in [18
]. The previous work related to the large-scale automatic collection of ATC audio data from different airports worldwide is covered in [15
]. Additionally, innovative research aimed at improving callsign recognition by integrating surveillance data into the pipeline is covered by [10
]. ASR systems are also employed for more high-level tasks such as pilot report extractions from very-high frequency (VHF) communications [51
]. Finally, multilingual ASR has also been covered in ATC applications in [19
The main components of a hybrid system are a pronunciation lexicon, LM and AM. One key advantage of a hybrid system versus other ASR techniques is that the text data (e.g., words, dictionary) and pronunciation of new words are collected and added beforehand, hoping to match the target domain of the recognizer. Standard hybrid-based ASR approaches still rely on word-based lexicons, i.e., missing or out-of-vocabulary words from the lexicon cannot be hypothesized by the ASR decoder. The system is composed of an explicit acoustic and language model. A visual example of hybrid-based ASR systems is in the bottom panel of Figure 2
. Most of these systems can be trained with toolkits such as Kaldi [52
] or Pkwrap [53
End-to-End Automatic Speech Recognition. End-to-end (E2E) systems are based on a different paradigm compared to hybrid-based ASR. E2E-ASR aims at directly transcribing speech to text without requiring alignments between acoustic frames (i.e., input features) and output characters/words, which is a necessary separate component in standard hybrid-based systems. Unlike the hybrid approach, E2E models are learning a direct mapping between acoustic frames and model label units (characters, subwords or words) in one step toward the final objective of interest.
Recent work on encoder–decoder ASR can be categorized into two main approaches: connectionist temporal classification (CTC) [54
] and attention-based encoder–decoder systems [55
]. First, CTC uses intermediate label representation, allowing repetitions of labels and occurrences of ‘blank output’, which labels an output with ‘no label’. Second, attention-based encoder–decoder or only-encoder models directly learn a mapping from the input acoustic frames to character sequences. For each time step, the model emits a character unit conditioned on the inputs and the history of the produced outputs. The important lines of work for E2E-ASR can be categorized as self-supervised learning [56
] for speech representation, covering bidirectional models [26
] and autoregressive models [60
Moreover, recent innovative research on E2E-ASR for the ATC domain is covered in [62
]. Here, the authors follow the practice of fine-tuning a Wav2Vec 2.0 model [26
] with public and private ATC databases. This system reaches on-par performances with hybrid-based ASR models, demonstrating that this new paradigm for ASR development also performs well in the ATC domain. In E2E-ASR, the system encodes directly an acoustic and language model, and it produces transcripts in an E2E manner. A visual example of an only-encoder E2E-ASR system is in the top panel of Figure 2
2.1.2. Natural Language Understanding
Natural language understanding (NLU) is a field of NLP that aims at reading comprehension. In the field of ATC, NLU is related to intent detection and slot filling. The slot-filling task is akin to named entity recognition (NER). In intent detection, the commands from the communication are extracted, while slot filling refers to the values of these commands and callsigns. Furthermore, throughout the paper, the system that extracts the high-level ATC-related knowledge from the ASR outputs is called a high-level entity parser
system. The NER-based understanding of ATC communications has been previously studied in [11
], while our earlier work [25
] includes the integration of named-entity recognition (NER) into the virtual simulation-pilot framework.
The high-level entity parser
system is responsible for identifying, categorizing and extracting crucial keywords and phrases from ATC communications. In NLP, these keywords are classified into pre-defined categories such as parts of speech tags, locations, organizations or individuals’ names. In the context of ATC, the key entities include callsigns, commands and values (which includes units, e.g., flight level). For instance, consider the following transcribed communication (taken from Figure 3
The previous output is then used for further processing tasks, e.g., generating a simulation-pilot-like response, metadata logging and reporting, or simply to help ATCos in their daily tasks. Thus, NLU is mostly focused on NER [63
]. Initially, NER relied on the manual crafting of dictionaries and ontologies, which led to complexity and human error when scaling to more entities or adapting to a different domain [64
]. The advancement of ML-based methods for text processing, including NER, has been introduced by [65
]. The previous work [66
] continued to advance NER techniques. A high-level entity parser
system (such as ours) can be implemented by fine-tuning a pre-trained LM for the NER task. Currently, state-of-the-art NER models utilize pre-trained LMs such as BERT [28
], RoBERTa [67
] or DeBERTa [68
]. For the proposed virtual simulation-pilot, we use a fine-tuned BERT on ATC text data.
2.1.3. Response Generator
The response generator (RG) is a crucial component of the simulation-pilot agent. It processes the output from the high-level entity parser
system, which includes the callsign, commands and values uttered by the ATCo, and then later generates a spoken response. The response is then delivered in the form of a WAV file, which is played through the headphones of the ATCo trainee. Additionally, the response, along with its metadata, can be stored for future reference and evaluation. The RG system is designed to generate responses that are grammatically consistent with what a standard simulation-pilot (or pilot) would say in response to the initial commands issued by the ATCo. The RG system comprises three submodules: (i) grammar conversion, (ii) a word fixer (e.g., ATCo-to-pilot phrase fixer), and (iii) text-to-speech, also known as a speech synthesizer. A visual representation of the RG system split by submodules is in Figure 4
Grammar Conversion Submodule.
A component designed to generate the response of the virtual simulation-pilot. First, the output of the high-level entity parser
module (discussed in Section 2.1.2
) is input to the grammar conversion submodule. At this stage, the communication knowledge has already been extracted, including the callsign, commands and their values. This is followed by a grammar-adjustment process, where the order of the high-level entities is rearranged. For example, we take into account the common practice of pilots mentioning the callsign at the end of the utterance while ATCos mention it at the beginning of the ATC communication. Thus, our goal is to align the grammar used by the simulation-pilot with the communication style used by the ATCo. See the first left panel in Figure 4
Word Fixer Submodule.
This is a crucial component of the virtual simulation-pilot system that ensures that the output from the response generator aligns with the standard ICAO phraseology. This is achieved by modifying the commands to match the desired response based on the input communication from the ATCo. The submodule applies specific mapping rules, such as converting descend→descending
, to make the generated reply as close to standard phraseology as possible. Similar efforts have been covered in a recent study [39
] where the authors propose a copy mechanism
that copies the key entities from the ATCo communication into the desired response of the virtual simulation-pilot, e.g., maintain→maintaining
. In real-life ATC communication, however, the wording of ATCos and pilots slightly differs. Currently, our word fixer
submodule contains a list of 18 commands but can be easily updated by adding additional mapping rules to a rules.txt
file. This allows the system to adapt to different environments, such as aerodrome control, departure/approach control or area control center. The main conversion rules used by the word fixer submodule are listed in Table 1
. The ability to modify and adapt the word fixer submodule makes it a versatile tool for training ATCos to recognize and respond to standard ICAO phraseology. See the central panel in Figure 4
Speech synthesis, also referred to as text-to-speech (TTS), is a multidisciplinary field that combines various areas of research such as linguistics, speech signal processing and acoustics. The primary objective of TTS is to convert text into an intelligible speech signal. Over the years, numerous approaches have been developed to achieve this goal, including formant-based parametric synthesis [69
], waveform concatenation [70
] and statistical parametric speech synthesis [71
]. In recent times, the advent of deep learning has revolutionized the field of TTS. Models such as Tacotron [72
] and Tacotron2 [73
] are end-to-end generative TTS systems that can synthesize speech directly from text input (e.g., characters or words). Most recently, FastSpeech2 [29
] has gained widespread recognition in the TTS community due to its simplicity and efficient non-autoregressive manner of operation. Finally, TTS is a complex field that draws on a variety of areas of research and has made significant strides recently, especially with the advent of deep learning. For a more in-depth understanding of the technical aspects of TTS engines, readers are redirected to [74
] and novel diffusion-based TTS systems in [75
]. The TTS system for ATC is depicted in the right panel in Figure 4
2.2. Optional Modules
In contrast to the base modules, covered in Section 2.1
, the optional modules are blocks that can be integrated into the virtual simulation-pilot to enhance or add new capabilities. An example is the PTT (push-to-talk) signal. In some cases a PTT signal is not available; thus, voice activity detection can be integrated. Below, each of the proposed optional modules is covered in more detail.
2.2.1. Voice Activity Detection
Voice activity detection (VAD) is an essential component in standard speech-processing systems to determine which portions of an audio signal correspond to speech and which are non-speech, i.e., background noise or silence. VAD can be used for offline purpose decoding, as well as for online streaming recognition. The offline VAD is used to split a lengthy audio into shorter segments that can then be used for training or evaluating ASR or NLU systems. The online VAD is particularly crucial for ATC ASR when the PTT signal is not available. An example of an online VAD is the WebRTC (developed by Google https://webrtc.org/
, accessed on 12 May 2023). In ATC communications, VAD is used to filter out the background noise and keep only the speech segments that carry the ATCo’s (or pilot’s) voice messages. One of the challenges for VAD in ATC communications is the presence of a high level of background noise. The noise comes from various sources, e.g., the engines of aircraft, wind or even other ATCos. ATC communications can easily have signal-to-noise (SNR) ratios lower than 15 dB. If VAD is not applied (and there is not a PTT signal available), the ASR system may degrade the accuracy of speech transcription, which may result in incorrect responses from the virtual simulation-pilot agent.
VAD has been explored before in the framework of ATC [76
]. A general overview of recent VAD architecture and research directions is covered in [77
]. Some other researchers have targeted how to personalize VAD systems [78
] and how this module plays its role in the framework of diarization [79
]. There are several techniques used for VAD, ranging from traditional feature-based models to hidden Markov models to Gaussian mixture-based models [80
]. On the other hand, machine-learning-based models have proven to be more accurate and robust, particularly deep neural network-based methods. These techniques can learn complex relationships between the audio signal and speech and can be trained on large annotated datasets. For instance, convolutional and deep-neural-network-based VAD has received much interest [76
]. VAD can be used in various stages of the ATC communication pipeline. VAD can be applied at the front-end of the ASR system to pre-process the audio signal and reduce the processing time of the ASR system. Figure 1
and Figure 2
depict where a VAD module can be integrated into the virtual simulation-pilot agent.
2.2.2. Contextual Biasing with Surveillance Data
In order to enhance the accuracy of an ASR system’s predictions, it is possible to use additional context information along with speech input. In the ATC field, radar data can serve as context information, providing a list of unique identifiers for aircraft in the airspace called “callsigns”. By utilizing these radar data, the ASR system can prioritize the recognition of these registered callsigns, increasing the likelihood of correct identification. Callsigns are typically a combination of letters, digits and an airline name, which are translated into speech as a sequence of words. The lattice, or prediction graph, can be adjusted during decoding by weighting the target word sequences using the finite state transducer (FST) operation of composition [12
]. This process, called lattice rescoring, has been found to improve the recognition accuracy, particularly for callsigns. Multiple experiments using ATC data have demonstrated the effectiveness of this method, especially in improving the accuracy of callsign recognition. The results of contextual biasing are presented and discussed below in Section 4.1
Re-ranking module based on Levenshtein distance.
The high-level entity parser
system for NER (see Section 2.1.2
) allows us to extract the callsign from a given transcript or ASR 1-best hypotheses. Recognition of this entity is crucial where a single error produced by the ASR system affects the whole entity (normally composed of three to eight words). Additionally, speakers regularly shorten callsigns in the conversation, making it impossible for an ASR system to generate the full entity (e.g., ‘three nine two papa’
instead of ‘austrian three nine two papa’
, ‘six lima yankee’
instead of ‘hansa six lima yankee’
). One way to overcome this issue is to re-rank the entities extracted by the high-level entity parser
system with the surveillance data. The output of this system is a list of tags that match words or sequences of words in an input utterance. As our only available source of contextual knowledge is callsigns registered at a certain time and location, we extract callsigns with the high-level entity parser
system and discard other entities. Correspondingly, each utterance has a list of callsigns expanded into word sequences. As input, the re-ranking module takes (i) a callsign extracted by the high-level entity parser
system and (ii) an expanded list of callsigns. The re-ranking module compares a given n-gram sequence against a list of possible n-grams and finds the closest match from the list of surveillance data based on the weighted Levenshtein distance
. In order to use contextual knowledge, it is necessary to know which words in an utterance correspond to a desired entity (i.e., a callsign), which is why it is necessary to add into the pipeline the high-level entity parser
system. We skip the re-ranking in case the output is a ‘NO_CALLSIGN’ flag (no callsign recognized).
2.2.3. Read-Back Error-Insertion Module
The approach of using the virtual simulation-pilot system can be adapted to meet various communication requirements in ATC training. This includes creating a desirable read-back error (RBE), which is a plausible scenario in ATC, where a pilot or ATCo misreads or misunderstands a message [81
]. By incorporating this scenario in ATCos’ training, they can develop the critical skills for spotting these errors. This is a fundamental aspect of ensuring the safety and efficiency of ATM [82
]. The ability to simulate (by inserting a desired error) and practice these scenarios through the use of the virtual simulation-pilot system offers a valuable tool for ATCo training and can help to improve the overall performance of ATC. An example could look like: ATCo: turn right→Pilot (RBE): turning left
The structure of the generated RBE could depend on the status of the exercise, for instance, whether the ATCo trainee is in the aerodrome control or approach/departure control position. These positions should, in the end, change the behavior of this optional module. The proposed, optional RBE insertion module is depicted in Figure 5
5. Limitations and Future Work
In our investigation of ASR systems, we have explored the potential of hybrid-based and E2E ASR systems, which can be further enhanced by incorporating data relevant to the specific exercise undertaken by ATCo trainees, such as runway numbers or waypoint lists. Moving forward, we suggest that research should continue to explore E2E training techniques for ASR, as well as methods for integrating contextual data into these E2E systems.
The repetition generator currently in use employs a simple grammar converter and a pre-trained TTS system. However, we believe that additional efforts could be made to enhance the system’s ability to convey more complex ATC communications to virtual simulation-pilots. In particular, the TTS system could be fine-tuned to produce female or male voices, as well as modify key features such as the speech rate, noise artifacts or cues to synthesize voices in a stressful situation. Additionally, a quantitative metric for evaluating the TTS system could be integrated to further enhance its efficacy. We also list some optional modules (see Section 2.2
) that can be further explored, e.g., the read-back insertion module or voice activity detection.
Similarly, there is scope for the development of multimodal and multitask systems. Such systems would be fed with real-time ATC communications and contextual data simultaneously, later generating transcripts and high-level entities as the output. Such systems could be considered a dual ASR and high-level entity parser. Finally, the legal and ethical challenges of using ATC audio data are another important field that needs to be further explored in future work. We redirect the reader to the legal and privacy aspects for collection of ATC recordings
section in [11
In this paper, we have presented a novel virtual simulation-pilot system designed for ATCo training. Our system utilizes cutting-edge open-source ASR, NLP and TTS systems. To the best of our knowledge, this is the first such system that relies on open-source ATC resources. The virtual simulation-pilot system is developed for ATCo training purposes; thus, this work represents an important contribution to the field of aviation training.
Our system employs a multi-stage approach, including ASR transcription, a high-level entity parser system and a repetition-generator module to provide pilot-like responses to ATC communications. By utilizing open-source AI models and public databases, we have developed a simple and efficient system that can be easily replicated and adapted for different training scenarios. For instance, we tested our ASR system on different well-known ATC-related projects, i.e., HAAWAII, MALORCA and ATCO2. We reached as low as 5.5% WER on high-quality data (MALORCA, ATCo speech in operations room) and 15.9% WER on low-quality ATC audio such as the test sets from the ATCO2 project (noise levels below 15 dB).
Going forward, there is significant potential for further improvements and expansions to the proposed system. Incorporating contextual data, such as runway numbers or waypoint lists, could enhance the accuracy and effectiveness of the ASR and high-level entity parser modules. In this work, we evaluated the introduction of real-time surveillance data, which proved to further improve the system’s performance in recognizing and responding to ATC communications. For instance, our boosting technique brings a 9% absolute amelioration in callsign-detection accuracy levels (86.7% → 96.1%) for the NATS test set. It is also important to recall that additional efforts could be made to fine-tune the TTS system for the improved synthesis of male or female voices, as well as modifying the speech rate, noise artifacts and other features.
The proposed ASR system can reach as low as 5.5% and 15.9% word error rates (WERs) on high- and low-quality ATC audio (Vienna and ATCO2-test-set-1h, respectively). It is also proven that adding surveillance data to the ASR can yield a callsign detection accuracy of more than 96%. Overall, this work represents a promising first step towards developing advanced virtual simulation-pilot systems for ATCo training, and it is expected that future work will continue to explore this research direction.