Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application
Abstract
:1. Introduction
1.1. Air Traffic Control Safety
1.2. Spoken Instruction Understanding
- (a)
- ASR: translates the ATCO’s instruction from speech signal into text representation (human- or computer-readable). The ASR technique concerns the acoustic model, language model, or other contextual information.
- (b)
- LU: also known as text instruction understanding, with the goal to extract ATC-related elements from the text instruction since the ATC system cannot process the text directly, i.e., from text to an ATC-related structured data. The ATC elements are further applied to improve the operational safety of air traffic. In general, the LU task can be divided into three parts: role recognition, intent detection, and slot filling (ATC-related element extraction, such as aircraft identity, altitude, etc.).
1.3. Research Design
- (1)
- Purpose: presents the significance of the SIU task to clarify why we study it (Section 1.1).
- (2)
- Problem: presents the SIU system architecture (Section 1.2) and the difficulties we need to address to achieve the ASR task (Section 2).
- (3)
- (4)
- Application: introduces the potential application and benefits of applying the SIU task to real industrial systems (Section 4).
- Firstly, as mentioned before, thanks to a large amount of available industrial data storage and the development of deep learning techniques, the performance of concerned techniques of the SIU task are greatly improved in recent years. Therefore, this research mainly focuses on improving the deep learning-based approaches to achieve the SIU task.
- As is well known, the deep learning-based model is a kind of data-driven approach, which achieves the desired tasks (specifically, the pattern recognition tasks) by fitting the complicated distribution between the input and the output data. That is to say, the training data is essential to the deep learning model, whose performance highly depends on the quality of the training samples.
- Following the last description, the analysis of the task specificities of the SIU task in the ATC domain will focus on the input and output of the SIU techniques, i.e., ASR, LU, and VPR. In general, the input and output of the SIU model consist of the ATC speech, vocabulary, and ATC-related elements, as can be found in Figure 1.
- In succession, based on the production mechanism of the ATC speech and ATC rules, a systematic analysis for the task specificities is achieved from various perspectives.
1.4. Document Structure
2. Challenges
2.1. Data Collection and Annotation
2.2. Volatile Background Noise and Inferior Intelligibility
- Due to the resource limitation of the radio transmission, an ATCO usually communicates with several pilots in the same communication frequency. Therefore, the equipment and radio transmission conditions change as the speaker changes [27], which further results in volatile background noise in the same frequency, as shown in Figure 3. It is clear that the feature intensities distribute in different frequency ranges due to the different noise models (communication equipment or conditions).
- In general, the speech signal of ATC communication is recorded in a very low sample rate (8000 Hz), which degenerates the intelligibility of the speech.
- Since the spoken instruction is transmitted by radio communication, the robustness of the communication is always a fatal obstacle to receive high-quality speech for both ATCOs and pilots in the ATC domain.
- In general, the speech rate of ATC speech is higher than that in daily life due to the time constraints of the traffic situation. This fact severely damages the quality and intelligibility of the ATC speech. For example, speaking “two two” in a fast speech rate may probably cause an overlapped speech segment, and the ASR system can only output one “two” (incorrect results).
2.3. Unstable Speech Rate
- Traffic situation: the ATCO unconsciously speeds up his speech when facing a busy sector or peaking hours.
- Language: the ATCOs usually speak their native language at a higher speech rate than that of other languages. For example, ATCOs in China speak Chinese at a higher speech rate than English.
- Emotion: The speech rate is also impacted by the ATCO’s emotion and presents an irregular and unstable state.
2.4. Multilingual and Accented Speech
2.5. Code Switching
2.6. Vocabulary Imbalance
2.7. Generalization of Unseen Samples
2.8. Ambiguous Word Meaning
- (a)
- Since digits are commonly used in the speech corpus, the distributions or the contextual correlations between digits and other words are extremely similar. This fact reduces the effectiveness of the language model (LM) for text correction to a certain extent for the speech recognition and language understanding task.
- (b)
- For the LU task, it is hard to design a fair and distinct label (slot filling) for digits in the ATC-related corpus. If all the digits are regarded as the same label (i.e., digit), the actual role for different goals (airline number, flight level, altitude, etc.) will be confused. If all the digits are explained as different labels based on their real goal, a large amount of one-to-many relationships will be generated. Both situations have a possibility of degenerating the final performance of the LU model.
2.9. Role Recognition
2.10. Contextual Information
3. Technique
3.1. Automatic Speech Recognition
- (1)
- Statistical models: The introduction of statistical models advanced the first technical peak of the ASR research, which achieves the goal of large vocabulary continuous speech recognition (LVCSR). The hidden Markov model (HMM) [29] was proposed to capture the state transitions among continuous phonemes, while the Gaussian mixture model (GMM) was applied to build the distribution between the state and the vocabulary unit [30]. Currently, the HMM/GMM framework still plays an important role in the ASR research.
- (2)
- Hybrid neural network models: Thanks to the improvement of the deep neural network (DNN), it was also proposed to the ASR research to replace the GMM, which further generates the HMM/DNN framework [31]. As expected, the HMM/DNN showed desired performance improvements over the HMM/GMM framework, which also promotes the ASR research into the deep learning era.
- (3)
- End-to-end models: Due to the strict requirements of the alignment between speech and vocabulary, Graves et al. proposed a novel loss function called connectionist temporal classification (CTC) [32]. The CTC loss function also formulated a new framework, i.e., it is also known as the end-to-end-based ASR model. The end-to-end ASR model is able to automatically align the speech and text sequence by inserting the blank label, which formulates a more intuitive pipeline [33,34]. The end-to-end framework reduces the requirement of expertise-dependent knowledge and greatly promotes the popularization of the ASR study for common researchers. Many outstanding research outcomes were obtained based on this framework, such as Deep speech 2 (DS2) [35], Jasper [36], CLDNN [37], DeepCNN [38], etc.
- (4)
- Sequence-to-sequence models: Lately, the sequence-to-sequence (S2S) mechanism was also transferred to the ASR research [39,40]. Recently, the attention mechanism [41,42,43,44] and Transformer architecture [45,46,47,48] were also improved to address the ASR issues and showed desired performance improvement.
3.2. Language Understanding
- (1)
- Role recognition: details as illustrated in Section 2.9.
- (2)
- Intent detection: extract the controlling intent (CI) from the text instruction. The CI is a set of predefined ATC-related classes, such as climb, descend, heading, etc.
- (3)
- Slot filling: analyze every word in a text instruction to obtain the contextual types, which are called instruction elements (IE). Similarly, the IE is also a set of predefined ATC-related classes, such as airline, flight number, altitude, speed, etc.
- (1)
- Intent detection: It is a classification task. Various models were proposed and improved to achieve this task, including the generative machine learning models (such as Bayesian [68], HMM [69], etc.), and discriminative models (such as logistic regression [70], maximum entropy [71], conditional random fields (CRF) [72], support vector machine (SVM) [73], etc.). Deep learning models, including recurrent neural network (RNN) [74] and convolutional neural network (CNN) [75], were also introduced to achieve the intent detection.
- (2)
- Slot filling: A maximum entropy Markov model (MEMM) [76] was proposed to achieve the information extraction and segmentation from texts. The CRF was also improved to achieve the slot filling task in [72]. The RNN block [77] and long short-term memory (LSTM) [78] were also applied to improve the performance by building long-term dependencies among the input text sequence.
- (3)
- Joint model: Liu et al. proposed an LSTM-based model to achieve intent detection and the slot-filling task jointly [79]. A combined model based on the CNN and triangular CRF was also improved to jointly achieve the SLU task [75]. The recursive neural networks (RecNN) architecture [80] and gated recurrent unit (GRU) [81] were studied to obtain the semantic utterance classification and slot filling jointly. Recently, the attention mechanism [82,83] and transformer architecture [84,85] were also proposed to address existing issues in the SLU research.
3.3. Voiceprint Recognition
- (1)
- Template matching: In the early stage, the VPR approaches directly calculated the similarity between the time-frequency spectrum to determine whether two utterances come from the same speaker [89]. Then, this type of approach was improved to consider the speech diversity in the temporal dimension, which further generated other approaches, such as the dynamic time warping (DTW) [90] and vector quantization (VQ) [91], etc.
- (2)
- Statistical models: As the GMM model has made great progress in the ASR research, it was also explored to build a robust text-independent VPR system [92]. Moreover, other models were further introduced to improve the performance and robustness, such as the universal background model [93] and support vector machine [94].
- (3)
- (4)
- Deep neural network models: With the d-vector [97] proposed in 2014, DNN-based models showed the ability to directly optimize the discriminations among different speakers. Subsequently, both metric learning and representation learning were also widely used in the VPR research. In this pipeline, the DNN architecture is used to extract high-level abstract embeddings as voiceprint representation features, while metric learning is applied to optimize the networks. Enormous research outcomes were generated based on this core idea, such as Deep Speaker [98], X-vector [99,100], j-vector [101,102], SincNet [103], etc.
4. Applications
4.1. Information Enhancement
4.2. Communication Error Detection (CED)
- (1)
- The instruction completeness: Confirm whether essential elements are embedded in the ATCO’s instruction based on the ATC rules, such as target altitude for climb instruction. The purpose of this application is to encourage the ATCO to issue standard instructions to eliminate misunderstandings between ATCO and aircrew during the ATC communication.
- (2)
- Resource incursion: Check whether the concerned resources of the ATCO instruction are valid or have conflicted with other operators from temporal and spatial dimensions, such as the closed runway detection, ground obstacle, etc. [104]. In this way, the potential risks can be detected in the stage of instruction issue and greatly improve the operational safety in advance.
- (3)
- Repetition check: Check whether the pilot receives the ATCO’s instruction in a correct and prompt manner. The repetition check error includes no response from aircrew, repetition error (intent or elements), etc. [5]. This application is able to reduce the risks raised by the incorrect transmission and understanding of the pilot instruction, which can eliminate the potential safety risk during the issue of instruction (before changing the aircraft motion states).
4.3. Conflict Detection Considering Intent
4.4. Post Analysis and Processing
- (1)
- Workload measurement: Evaluate the workload of an ATCO from the time and sector dimensions, such as flight peak hours or busy sectors [13,109]. Based on the evaluation results, more efficient and effective designs for the airspace sector are expected to be achieved to balance the ATCO workload, which is also helpful to improve the operational safety of air traffic. For instance, a frequent “correct” instruction may indicate that an ATCO is in a fatigued state, so that too many incorrect instructions appeared in the ATC speech.
- (2)
- Performance evaluation: The ATC speech is a side view of real-time air traffic operation, in which the ATCO performance is enclosed as the conversation speech. Thus, the ATCO performance also deserves to be considered to detect the improper ATC actions and further improve ATCO’s skills. For example, excessive extra instructions for changing aircraft motion state may indicate that the sector always faces potential risk so that the ATCO has to adjust the aircraft motion to resolve the potential conflict. Facing this situation, it is necessary to improve operational safety by enhancing the ATCO skills or designing a more proper standard operating procedure (SOP) during the ATC communication.
- (3)
- Information retrieving: Currently, human hearing is the only way to search the ATC speech for a certain goal. Intuitively, based on the SIU technique, it is easy to search the target information (speech) from a long-duration continuous record speech, such as a certain flight number or a certain ATCO. This is strong support to the post-incident analysis in an automatic manner, since it is laborious and costly work undertaken by human staff.
- (4)
- Event detection: Detect anomaly speech to support other analyses in the ATC domain. For instance, the “confirm” instruction is issued by many speakers in a certain sector or time period and may indicate that the communication condition between ATCO and aircrew in the sector or time period is needed to be improved, such as the infrastructure malfunction or signal interference.
4.5. ATCO Training
5. Future Research
5.1. Speech Quality
- (1)
- Speech enhancement: Facing the inferior speech quality in the ATC domain, an intuitive way is to achieve the speech enhancement to further improve the ASR and VPR performance. With this technique, a high-quality ATC speech is expected to be obtained to support the SIU task and further benefit to achieve the high-performance subsequent ATC applications.
- (2)
- Representation learning: Facing the diverse distribution of speech features raised by different communication conditions, devices, multilingual, unstable speech rate, etc., there are reasons to believe that the handcrafted feature engineering algorithms (such as MFCC) may fail to support the ASR and VPR research to obtain the desired performance. The representation learning, i.e., extracting speech features by a well-optimized neural network, may be a promising way to improve the final SIU performance.
5.2. Sample Scarcity
- (1)
- Transfer learning: Although a set of standardized phraseology has been designed for the ATC procedure, the rules and vocabulary still depend on the flight phases, locations, and control centers. It is urgent to study the transfer learning technique among different flight phases, locations, and control centers to save the sample requirement and formulate a unified global technical roadmap.
- (2)
- Semi-supervised and self-supervised research: Since the data collection and annotation is always an obstacle of applying advanced technology to the ATC domain, the semi-supervised and self-supervised strategies are expected to be a promising way to overcome this dilemma, in which the unlabeled data samples can also be applied to contribute the model optimization based on their intrinsic characteristics, such as that in the common application area.
- (3)
- Sample generation: Similar to the last research topic, sample generation is another way to enhance the sample size and diversity and further improve the task performance, such as text instruction generation.
5.3. Contextual Information
- (1)
- Contextual situational incorporation: As illustrated before, contextual situational information is a powerful way to improve SIU performance. Due to the heterogeneous characteristics of the ATC information, existing works failed to take full advantage of this type of information. Learning from the state-of-the-art studies, the deep neural network may be a feasible tool to fuse the multi-modal input by encoding them as a high-level abstract representation using the learning mechanism and further make contributions to improve the SIU performance.
- (2)
- Multi-turn dialog management: Obviously, the ATC communication in the same frequency is a multi-turn and multi-speaker dialog with a task-oriented goal (ATC safety). During the dialog, the historical information is able to provide significant guidance to current instruction based on the air traffic evolution. Thus, it is important to consider the multi-turn history information to enhance the SIU task of current dialog, similar to what is required in the field of natural language processing.
5.4. Other Research Topics
- (1)
- Joint SIU model: Currently, the ASR and LU tasks are achieved separately, i.e., a cascaded pipeline, which also leads to cascaded errors (reduces the overall confidence). In the future, a joint SIU model for automatic speech recognition understanding (ASRU) deserves to be studied to capture the task compatibility to promote the final performance, similar to that of the joint SLU model. In this way, the SIU task can be achieved in a more intuitive and clear processing paradigm.
- (2)
- On-board SIU system: Currently, all the SIU studies are developed based on the requirements of the ground systems. The computational resource is heavily required due to the applications of the deep learning model. For future development, it is also attractive to achieve the SIU task for the on-board purpose (i.e., cockpit) and further construct a safety monitoring framework for the aircrew. In this way, a bi-directional safety-enhancing system is constructed for both the ATCO and aircrew, which is expected to ensure flight safety in a reinforced manner. To this end, the model transfer from the X86 platform to the embed system (such Jetson, NVIDIA, CA, USA) is the primary research to save the computational resource requirements, such as model compression, power reduction, etc.
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Corpus | Language | Domain | Size (Hour) | Access |
---|---|---|---|---|---|
1 | LibriSpeech [17] | English | novels | 960 | public |
2 | TED-LIUM3 [18] | English | TED talks | 452 | public |
3 | Switchboard [19] | English | Telephone | 260 | public |
4 | THCHS30 [20] | Chinese | newspapers | 30 | public |
5 | AISHELL-V1 [21] | Chinese | multidomain | 500 | public |
6 | AISHELL-V2 [22] | Chinese | multidomain | 1000 | application |
7 | ATCSpeech [23] | Chinese/English | real ATC | 59 | application |
8 | ATCOSIM [24] | English | simulated ATC | 11 | public |
9 | LDC94S14 [25] | English | airport | 70 | paid |
10 | Airbus [26] | English | pilot | 40 | unavailable |
Language | Corpus | Mean (w/s) | Standard Deviation (w/s) |
---|---|---|---|
Chinese | ATCSpeech [23] | 5.15 | 1.10 |
THCHS-30 [20] | 3.48 | 0.47 | |
English | ATCSpeech [23] | 3.28 | 0.75 |
Librispeech [17] | 2.73 | 0.47 |
Language | Common | ATC |
---|---|---|
English | three | tree |
five | fife | |
nine | niner | |
thousand | tousand | |
Chinese | ling | dong |
yi | yao | |
er | liang | |
qi | guai |
Papers | Technique Details | Challenges Concerned |
---|---|---|
[1] | Independent end-to-end models, DS2, ASR, ATC safety monitoring | Section 2.4 and Section 2.5 |
[27] | An integrated cascaded model, DS2+S2S, multilingual ASR | Section 2.2, Section 2.3, Section 2.4, Section 2.5, Section 2.6 and Section 2.7 |
[16] | Independent end-to-end models, DS2, ASR, pretraining, transfer learning | Section 2.1, Section 2.4, Section 2.5 and Section 2.6 |
[8] | Cascaded model, DS2+S2S, SIU, multi-level LMs | Section 2.2, Section 2.4, Section 2.5, Section 2.8 and Section 2.9 |
[54] | Complete end-to-end model, representation learning, multilingual, pretraining | Section 2.1, Section 2.2, Section 2.3, Section 2.4, Section 2.5 and Section 2.6 |
[55] | HMM/DNN, data augmentation, iterative training using unlabeled samples | Section 2.1 and Section 2.7 |
[56] | HMM/DNN, context-aware rescoring, SIU task | Section 2.5, Section 2.7, Section 2.8 and Section 2.10 |
[57] | Semi-supervised for transfer learning, DNN based models | Section 2.1, Section 2.2, Section 2.5 and Section 2.7 |
[14] | AcListant® based traffic dynamic sensing, Arrival Managers (AMAN) | Section 2.7 and Section 2.8 |
[11] | Cross-task adaption, HMM framework, transfer learning | Section 2.2, Section 2.3 and Section 2.7 |
[58] | N-best list re-ranking, ATCOSIM, syntactic knowledge | Section 2.10 |
[59] | traffic dynamic sensing | - |
[12] | HMM-based framework, Spanish and English ATC speech, SIU task | Section 2.4 and Section 2.5 |
[60] | French-accented English ATC speech, Time Delay Deep Neural Network (TDNN) | Section 2.1, Section 2.2, Section 2.4 and Section 2.5 |
[61] | 170 h of ATCO speech, TDNN-based benchmark | Section 2.2, Section 2.4 and Section 2.7 |
[62] | Improve intelligibility by reducing speech rate | Section 2.2 and Section 2.3 |
[63] | Speech corpus for ASR and text-to-speech task | Section 2.2, Section 2.3 and Section 2.5 |
[64] | Callsign correlation between ATC speech and surveillance data | Section 2.1, Section 2.5 and Section 2.10 |
Text | CCA | 4012 | Turn Left | Heading 330 | Climb to | 1200 m |
---|---|---|---|---|---|---|
Slot filling | B-AL | B-CS | B-TL | I-TL | B-CL | I-CL |
Intent | Turn left and climb |
Papers | Technique Details |
---|---|
[1] | Joint S2S model, 26 controlling intent, safety monitoring |
[8] | Joint S2S model, 26 controlling intents, 55 instruction elements |
[10] | SESAR 2020 Solution PJ.16-04, extra qualifier, conjunction |
[86] | 10 ontologies for ATC command extraction |
[57] | More label classes (such as QNH), conditional clearances |
[87] | Command definition for ASR rescoring |
Section | Item | Findings | Conclusions or Future Research Topics |
---|---|---|---|
Challenges | Data collection and annotation | English corpus [24,26] Chinese/English corpus [23] | More corpora are required to build large-scale SIU systems in the ATC domain. |
Volatile background noise and inferior intelligibility | Multi-scale CNN [27] | Representation learning may be a promising way to overcome the mentioned issue. | |
Unstable speech rate | Multi-scale CNN [27] | ||
Multilingual and accented speech | Cascaded pipeline [8,27] Independent system [1,23] | The end-to-end multilingual framework. | |
Code switching | Language model [27] | The author believes that the most efficient way is to build sufficient training samples. | |
Vocabulary imbalance | Phoneme-based vocabulary [8,27] Data augmentation [1,26] | Sub-word-based vocabulary is a better tradeoff between the vocabulary size and sequence length. | |
Generalization of unseen samples | Transfer learning [16] | Transfer learning from other domains is a feasible way to address this issue. | |
Ambiguous word meaning | Currently, no literature is for this issue. | An intuitive way is to build a dictionary for synonyms pairs. | |
Role recognition | Text-dependent SLU model [1,8] | VPR is a powerful text-independent way to achieve this task. | |
Contextual information | Enumeration of possible information [56,87]. | Deep information fusion using neural network is expected to improve the performance of this issue. | |
Techniques | Automatic speech recognition | Monolingual: HMM-based [26], deep learning based [1,16,23,60]. Multilingual: deep learning based [8,27,54]. | Great efforts deserve to be made to promote the ASR task into an industrial level, including speech quality, contextual information, etc. |
Language understanding | Concept extraction [10], deep learning based SLU model [1,8]. | More concept classes are required to cover the ATC-related elements, especially for the rarely used terms. | |
Voiceprint recognition | Currently, there is no literature for this issue. | Building a corpus for the ATC environment is the key to train a qualified VPR system. | |
Applications | Information enhancement | Electronic strip system [10]. | More applications are expected to be achieved based on the SIU task. |
Communication error detection | Studies based on ASR tools [5,12,14,60,62]. | A way to improve the air traffic safety. | |
Conflict detection considering intent | Flight trajectory considering intent [107]. | Conflict detection considering intent should be studied to provide more warning time for ATCO. | |
Post analysis and processing | (1) Workload measurement and performance evaluation [13,109]. (2) Currently, there is no literature on the information retrieving and event detection. | More applications are required to be explored to take full advantage of the SIU research outcomes. | |
ATCO training | There is no literature for this issue. | It is very important to emphasize the SIU task in the ATC domain. |
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Lin, Y. Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application. Aerospace 2021, 8, 65. https://doi.org/10.3390/aerospace8030065
Lin Y. Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application. Aerospace. 2021; 8(3):65. https://doi.org/10.3390/aerospace8030065
Chicago/Turabian StyleLin, Yi. 2021. "Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application" Aerospace 8, no. 3: 65. https://doi.org/10.3390/aerospace8030065
APA StyleLin, Y. (2021). Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application. Aerospace, 8(3), 65. https://doi.org/10.3390/aerospace8030065