Cockpit Speech Recognition Latency Reduction via Pre-flight Voice Profiling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems in aircraft cockpits face challenges in achieving high accuracy due to background noise and require costly, time-intensive offline training or latency-prone real-time training methods.
Innovation Solution
A method that uses a carry-on-device to capture and preprocess voice data during pre-flight checks, extracting features and adapting them with a speaker-independent speech recognition system onboard the aircraft, reducing latency and improving accuracy without the need for costly hardware changes in the cockpit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline training is performed for every pilot and co-pilot to improve speech recognition accuracy, then accuracy is improved, but the process becomes costly, laborious, and time-intensive
Solution Approach 1:
The system performs preliminary voice profile extraction during pre-flight check-in procedures, capturing voice data before the actual flight begins. This allows the speech recognition system to be pre-adapted to the pilot's voice characteristics without requiring extensive dedicated training sessions, thus improving accuracy while minimizing time loss.
Solution Approach 2:
The speech recognition system automatically extracts voice features and adapts to the pilot's voice during routine pre-flight check-in procedures, without requiring the pilot to dedicate additional time for separate training sessions. The system serves itself by utilizing existing operational workflows to gather training data.
2Measurement precision
If real-time training is implemented in the cockpit to improve speech recognition accuracy, then accuracy is improved, but latency increases and additional processing and memory are required
Solution Approach 1:
Voice profile extraction is performed during pre-flight check-in procedures before the flight begins, rather than during the flight itself. This preliminary extraction eliminates latency during actual flight operations while still providing real-time adaptation capabilities when needed.
Solution Approach 2:
The system extracts only the essential voice features from the pilot's speech during check-in, separating these features from the full voice data. This extraction reduces the amount of data that needs to be processed and stored, thereby reducing latency and processing requirements during actual flight operations.
3Object-affected harmful factors
If current noise correction technology is used in the cockpit to improve speech recognition, then some background noise is corrected, but the system still suffers from high background noise and several problems
Solution Approach 1:
Voice profiles are extracted during pre-flight check-in procedures in environments with controlled noise levels, before the aircraft engines start and high background noise begins. This preliminary extraction in quieter conditions improves the quality of voice features while avoiding the harmful effects of cockpit background noise.
Data Source
AI summary
A method for implementing a speaker-independent speech recognition system with reduced latency is provided. The method includes capturing voice data at a carry-on-device from a user during a pre-flight check-in performed by the user for an upcoming flight; extracting features associated with the user from the captured voice data at the carry-on-device; uplinking the extracted features to the speaker-independent speech recognition system onboard the aircraft; and adapting the extracted features with an acoustic feature model of the speaker-independent speech recognition system.