Control System Training via Non-Lexical Audio and State Changes
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Solution Overview
Problem
Conventional control systems struggle to associate non-lexical or interjectional audio inputs with specific executable functions, as they are not equipped to recognize and link these inputs with corresponding state changes.
Innovation Solution
The system receives non-lexical or interjectional audio inputs and correlates them with state change indications within a predefined time frame, using an associative data structure to store audio inputs, contextual information, and state change indications, and adjusts a confidence factor for match associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional control systems use known voice commands for training, then the system can associate commands with executable functions, but the system cannot process non-lexical or interjectional audio inputs
Solution Approach 1:
The control system performs self-training by automatically associating non-lexical audio inputs with state changes without requiring pre-programmed commands. The system monitors its own environment, detects state changes, and autonomously builds an associative database, enabling it to process interjectional inputs while maintaining manageable complexity through self-organization rather than external programming
Solution Approach 2:
The system changes the parameter of audio input recognition from requiring lexical matching to accepting non-lexical patterns. By transforming the recognition parameter from strict command matching to pattern association based on acoustic characteristics and contextual state changes, the system gains versatility to process interjections while using confidence factors to manage the complexity of broader recognition
2Reliability
If the system stores all audio inputs and state changes indefinitely, then training data is comprehensive, but storage requirements and processing overhead increase
Solution Approach 1:
The system performs preliminary filtering by storing only state change indications that occur within a predefined time window after an audio input. This preliminary action of time-bounded association ensures that only relevant state changes are captured, maintaining reliable training data while preventing unnecessary accumulation of unrelated data, thus balancing accuracy with data quantity management
Solution Approach 2:
The system applies partial action by selectively storing only those state changes that meet the time window criterion rather than all possible state changes. This partial recording approach provides sufficient training data for reliable function execution while avoiding the excessive data storage and processing that would result from capturing every state change indefinitely
3Productivity
If the system executes functions based on match associations, then responsiveness to audio inputs improves, but false executions may occur with low-confidence matches
Solution Approach 1:
The system uses dynamic confidence factors that can be adjusted based on the quality of match associations. Rather than using a static threshold, the confidence factor dynamically adapts to distinguish high-quality matches from false positives, enabling the system to execute functions rapidly when confidence is high while maintaining reliability by filtering out low-confidence associations that could lead to false executions
Data Source
AI summary
Systems and methods are disclosed herein for training a control system based on prior audio inputs. The disclosed systems and methods receive a non-lexical or interjectional audio input. State change indications are also received and stored by the system within a predefined period of time starting from the time the system received the audio input. The system then receives a subsequent audio input. If the audio inputs of both the audio input and the subsequent audio input match, and contextual information for the audio input and the subsequent audio input match, the system stores a match association, comprising a confidence factor, for the subsequent audio input to the audio input in the associative data structure. If the confidence factor is greater than a preconfigured confidence level, the system executes one or more functions based on stored state change indications.