Modular Arbitration for Extensible Speech Recognition
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Solution Overview
Problem
Conventional multi-class classifiers in speech recognition UI systems are not extensible for independently developed applications, limiting their ability to handle new or unknown applications effectively.
Innovation Solution
A modular UI system architecture where each module implements a subset of the UI functionality, allowing independent development and arbitration by a single system that analyzes user requests and prioritizes relevant modules based on probability outputs from classifiers trained using a shared reference dataset, enabling extensibility without modifying existing modules or the arbitration system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-class classifier is used for arbitration in speech recognition UI systems, then arbitration performance for known applications is improved, but extensibility for independently developed applications deteriorates
Solution Approach 1:
The system segments the arbitration function into independent module-specific classifiers rather than a single multi-class classifier. Each module maintains its own classifier that independently evaluates user inputs, allowing modules to be developed and updated independently while maintaining arbitration performance through modular, interchangeable components.
2Extent of automation
If a single arbitration system processes inputs from multiple applications, then centralized control is improved, but system complexity deteriorates
Solution Approach 1:
Each module performs self-service by maintaining its own classifier that independently evaluates user inputs and determines its own relevance. This eliminates the need for complex centralized arbitration logic, as each module autonomously assesses its suitability for handling the input, thereby maintaining centralized coordination while reducing overall system complexity.
3Productivity
If modules are developed independently, then development efficiency is improved, but integration complexity deteriorates
Solution Approach 1:
The system implements a universal arbitration framework where all modules interface through a common probability output mechanism. Each module's classifier produces a standardized probability indicating input relevance, allowing independently developed modules to be integrated without modification. This universal interface standard enables seamless integration while preserving independent development efficiency.
Data Source
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AI summary
Methods and apparatus to process a user input on independent applications that provide classifier outputs to an arbitration module, which selects one of the application to respond to the user input. The classifier outputs include a probability that the user input is in domain for the application functionality.