Autonomous ATC Voice Command Parsing with Criticality-Based Segmentation
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Solution Overview
Problem
Current speech recognition systems in aviation fail to meet certification standards for reliability, particularly in achieving a 10−5 failure rate in discerning air traffic control commands, and lack regulatory approval for aircraft certification.
Innovation Solution
A system that includes a processor configured to receive and transcribe ATC voice commands, categorize them by criticality level, and ensure accurate recognition through constrained grammar and an executive decision-making module, allowing for autonomous communication and decision-making with enhanced accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex speech recognition algorithms (Markov models, neural networks) are used to achieve high accuracy, then recognition accuracy improves, but device complexity increases and certification becomes difficult
Solution Approach 1:
The speech recognition system segments the ATC command into distinct grammatical components (call sign, command type, parameter values) and processes each component separately using dedicated parsing rules. This segmentation allows the system to achieve high accuracy through structured analysis rather than complex holistic models, making certification more feasible by providing clear, deterministic processing paths for each component.
2Productivity
If speech recognition systems operate autonomously without human intervention, then productivity improves, but reliability requirements become more stringent
Solution Approach 1:
The system implements feedback mechanisms where the autonomous communications manager requests clarification from ATC when command components are ambiguous or unclear. The system also provides feedback by confirming received commands and monitoring for inconsistencies. This feedback loop enables autonomous operation while maintaining reliability by ensuring understanding matches intent before execution.
Solution Approach 2:
The system performs preliminary actions by pre-defining grammatical structures, command types, and parameter ranges before actual communication occurs. The grammar parser is pre-configured with expected command formats and validation rules, allowing the system to autonomously process commands against predetermined frameworks rather than making real-time decisions about command structure, thereby improving both productivity and reliability.
3Adaptability or versatility
If the speech recognition system uses broad grammar coverage to handle diverse ATC commands, then adaptability improves, but measurement precision decreases
Solution Approach 1:
The system applies local quality by tailoring the grammar parser to handle specific command types with specialized rules for each component (call sign recognition, command type identification, parameter parsing). Each local component of the command receives customized processing appropriate to its nature, rather than using a single generic parsing approach. This allows broad adaptability across command types while maintaining high precision through domain-specific parsing rules for each component.
Data Source
AI summary
A system includes a radio configured to transmit and receive voice transmissions to and from air traffic control (ATC), the radio installed onboard an aircraft. The system includes a processor communicatively coupled to the radio and installed onboard the aircraft. The processor is configured to: receive an ATC voice command via the radio; transcribe the ATC voice command into an ATC command; and when the ATC command includes an aircraft identification associated with the aircraft, categorize the ATC command into components, each of the components being categorized based on a criticality level associated with said component, wherein the criticality level is or is associated with a required accuracy confidence level of transcribed speech for a given component.