Voice-Controlled UAV Semantic Analysis via Confidence-Based Language Modeling
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Solution Overview
Problem
Current speech recognition systems for UAV control interfaces are limited in their ability to handle spontaneous and stressed speech, due to inflexible language modeling and reliance on rule-based techniques that are not robust against recognition errors, leading to reduced accuracy and reliability in interpreting spoken commands.
Innovation Solution
A computer-implemented method that uses speech recognition software with confidence measures to analyze spoken instructions, identifying semantic concepts and providing tailored spoken confirmations based on confidence levels, employing continuous hidden Markov models and statistical language models like 2-gram models, and a bottom-up natural language understanding approach to improve accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If grammar-based language modelling is used, then ease of configuration is improved, but robustness against spontaneous speech deteriorates
Solution Approach 1:
The system dynamically changes the language modelling approach based on confidence measures. When speech recognition confidence is high, grammar-based modelling is used for ease of configuration. When confidence is low or speech is spontaneous/stressed, the system switches to statistical language modelling (N-gram) which provides robustness against unexpected word sequences, thus adapting parameters to resolve the contradiction.
Solution Approach 2:
The language modelling system is made dynamic by switching between grammar-based and statistical modelling based on real-time confidence assessment. This dynamic adaptation allows the system to leverage the simplicity of grammar-based models when appropriate while utilizing the robustness of statistical models when dealing with spontaneous speech, resolving the static contradiction between ease of configuration and robustness.
2Ease of manufacture
If rule-based natural language understanding is used, then ease of implementation is improved, but robustness against recognition errors deteriorates
Solution Approach 1:
The system incorporates confidence measures as feedback from the speech recognition module to the natural language understanding module. This feedback mechanism allows the NLU to adjust its processing strategy based on the reliability of the recognized words, enabling it to handle recognition errors more robustly while maintaining ease of implementation through structured rule-based processing enhanced by confidence-driven adaptation.
Solution Approach 2:
The system performs preliminary confidence assessment of recognized words before proceeding with natural language understanding. This preliminary action allows the system to identify potentially erroneous recognitions in advance and apply appropriate error handling strategies, thereby improving robustness against recognition errors while maintaining the ease of rule-based implementation.
3Ease of operation
If commercial speech recognition engines are used, then ease of integration is improved, but flexibility in language modelling deteriorates
Solution Approach 1:
The system achieves universality by making the commercial speech recognition engine perform multiple functions: both speech-to-text conversion and confidence-based language model selection. By layering additional language modelling capabilities on top of the commercial engine, the system maintains ease of integration while gaining the flexibility needed for spontaneous speech through the combined approach of grammar-based and statistical modelling.
Data Source
AI summary
The present invention relates to voice-activated vehicle control, and to the control of UAVs (unmanned air vehicles) using speech in particular. A method of controlling a vehicle is provided that includes receiving one or more instructions issued as speech and analyzing the speech using speech recognition software to provide a sequence of words and a word confidence measure for each word so recognized. The sequence of words is analyzed to identify a semantic concept corresponding to an instruction based on the analysis, and a semantic confidence level for the semantic concept identified derived at least in part with reference to the word confidence measures of the words associated with the semantic concept. A spoken confirmation of the semantic concept so identified based on the semantic confidence level is provided, and the semantic concept is used to provide a control input for the vehicle.