Aircraft Speech Recognition Model Parameter Extraction
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Solution Overview
Problem
Current command and control speech recognition systems in aircraft have limited vocabulary, restricting their use cases and response times, making it difficult to accommodate a wide range of commands and named entities without compromising accuracy and speed.
Innovation Solution
The system dynamically updates the command and control speech recognition models by extracting and adding acoustic and language model parameters from a more extensive air traffic control transcription model, allowing recognition of additional words relevant to the flight context, while maintaining fast response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the acoustic and language models are trained with a larger vocabulary to recognize more words and named entities, then the use cases and adaptability of the speech recognition system are extended, but the response time and processing speed deteriorate
Solution Approach 1:
The patent implements dynamic model updating where the acoustic and language models are not static but are continuously adapted during flight operations. The system processes flight context data in real-time and updates the command and control speech recognition models with relevant named entities and terminology specific to the current flight, making the vocabulary coverage dynamic rather than fixed.
Solution Approach 2:
The patent separates the speech recognition system into two distinct model sets: air traffic control transcription models with extensive vocabulary for general transcription tasks, and command and control models with limited vocabulary optimized for fast response. This segmentation allows each model to be specialized for its specific function, avoiding the need for a single large model to handle all scenarios.
Solution Approach 3:
The system performs preliminary processing of flight context data to identify and extract relevant named entities and terminology before they are needed for command recognition. By pre-processing and preparing the model updates in advance based on flight plan data, weather information, and other contextual data, the system ensures that when commands are issued, the models are already optimized for the current flight context.
2Loss of time
If the acoustic and language models are restricted to a smaller vocabulary, then the response time and processing speed are improved, but the use cases and adaptability are limited
Solution Approach 1:
The patent makes the command and control speech recognition system multi-functional by enabling it to adapt to different flight contexts and recognize various named entities dynamically. The same core model structure is used across different flights and scenarios, but it is customized with flight-specific vocabulary through continuous updates from flight context data, allowing universal applicability without sacrificing speed.
Solution Approach 2:
The system implements a feedback loop where the speech recognition model continuously processes flight context data, identifies relevant named entities and terminology, and updates itself with this information. This feedback mechanism ensures that the model remains current with the specific flight context while maintaining its fast processing capabilities, as the updates are incremental rather than requiring complete model retraining.
3Reliability
If the speech recognition system uses a comprehensive model for all scenarios, then the accuracy and coverage are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies local quality by making the model vocabulary and parameters specific to the local flight context rather than uniformly comprehensive. Each flight receives customized model updates based on its specific route, weather conditions, air traffic control procedures, and other contextual factors. This local optimization ensures high accuracy for each specific scenario without requiring a single overly complex universal model.
Solution Approach 2:
The system copies the effective approach from air traffic control transcription models to command and control models. By replicating the model structure and update methodology from the well-established ATC transcription system, the patent leverages proven techniques while adapting them for command recognition, reducing the need to develop entirely new complex systems from scratch.
Data Source
AI summary
Methods and systems for speech recognition in an aircraft are disclosed. Methods and systems include executing an air traffic control transcription application using first acoustic and language models and executing a command and control speech recognition application using second acoustic and language models. Flight context data is processed to identify additional words not included in training of the second acoustic and language model but included in training of the first acoustic and language models. Acoustic and language model parameters are extracted corresponding to the additional words from the first acoustic and language models. The extracted acoustic and language model parameters are added to the second acoustic and language models. An aircraft control command is generated that encapsulates at least one of the additional words using the command and control speech recognition application.