Context-Aware Speech Recognition Keyword Selection
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Solution Overview
Problem
Current speech recognition systems are inflexible and static in their use of keywords for controlling applications, as they do not adapt to different user contexts, leading to inadequate functionality in varying situations.
Innovation Solution
An application node detects the current user context and selects a predefined context that matches it, providing associated keywords to a speech recognition node, allowing for dynamic recognition and use of keywords as input to the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional speech recognition systems use fixed keywords for all situations, then the system structure remains simple, but the system cannot adapt to different user contexts leading to inadequate functionality
Solution Approach 1:
The patent implements dynamic keyword selection by detecting current user context and automatically selecting appropriate keywords from different predefined sets. The system transitions from static fixed keywords to dynamic context-dependent keywords, resolving the contradiction between adaptability and complexity by automating the selection process rather than requiring manual configuration of multiple keyword sets.
Solution Approach 2:
The system changes the parameter of keyword selection based on detected context parameters. Different context states (e.g., location, activity, time) trigger different keyword sets, allowing the system to adapt to various user situations without increasing structural complexity. The context detection and keyword selection are automated through parameter-based decision logic.
2Adaptability or versatility
If the system provides multiple predefined keyword sets for different contexts, then adaptability improves, but the complexity of managing and selecting appropriate keywords increases
Solution Approach 1:
The system performs self-service by automatically detecting user context and selecting appropriate keywords without requiring user intervention. The context detection module monitors user state, and the keyword selection module autonomously chooses the most relevant keyword set, eliminating the need for users to manually manage or switch between keyword sets. This resolves the contradiction by making the system self-managing rather than user-managed.
Solution Approach 2:
The system implements feedback loops where context detection results feed into keyword selection, which then feeds into speech recognition. The system continuously monitors context changes and adjusts keyword selection accordingly, creating a closed-loop control system that adapts automatically. This feedback mechanism resolves the contradiction by making keyword management automatic rather than manual.
3Measurement precision
If speech recognition uses context-aware dynamic keyword selection, then recognition accuracy in specific situations improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining multiple keyword sets corresponding to different contexts before runtime. When context detection identifies a particular situation, the system can immediately access the pre-prepared keyword set without performing complex real-time analysis. This resolves the contradiction by shifting computational effort from runtime to setup phase, maintaining fast response times while achieving high recognition accuracy.
Data Source
AI summary
Methods and nodes for enabling and producing input generated by speech of a user, to an application. When the application has been activated (2:1), an application node (200) detects (2:2) a current context of the user and selects (2:3), from a set of predefined contexts (204a), a predefined context that matches the detected current context. The application node (200) then provides (2:4) keywords associated with the selected predefined context to a speech recognition node (202). When receiving (2:5) speech from the user, the speech recognition node (202) is able to recognize (2:6) any of the keyword in the speech. The recognized keyword is then used (2:7) as input to the application.