Speech Recognition Resource Management via Voice Switching
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Solution Overview
Problem
Conventional speech recognition systems face challenges in distinguishing between dictation and commands, leading to user frustration and inefficient resource management, particularly when switching between different language models and tasks that require separate accounts and identification.
Innovation Solution
A distributed speech recognition system that allows seamless resource management and voice-activated command execution, enabling users to load specific speech resources without terminating existing logons, and automatically switching between applications and user profiles based on voice commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional speech recognition systems are used with separate accounts for different language models, then each language model can be accessed, but the system complexity and user frustration increase due to inability to switch between models seamlessly
Solution Approach 1:
The speech recognition system is designed to support multiple language models and user profiles within a single account framework. The system can dynamically switch between different language models (e.g., medical, legal, technical) and user profiles without requiring separate accounts, making the system universal across different domains and use cases while maintaining a unified access point
Solution Approach 2:
The system implements dynamic switching between language models and user profiles based on contextual cues from voice commands or application state. The language model selection is not static but adapts in real-time according to the user's needs, allowing seamless transitions between different specialized models without manual intervention or account switching
2Adaptability or versatility
If speech recognition system requires separate accounts for different tasks, then each task can have dedicated resources, but the ease of operation decreases due to need to terminate existing logon
Solution Approach 1:
The system pre-configures multiple user profiles and language models within a single account, preparing all necessary resources in advance. When a user needs to switch tasks, the system can quickly activate the pre-prepared profile and associated language model without requiring the user to log out and log back in, thus maintaining ease of operation while providing task-specific resources
Solution Approach 2:
The system merges multiple user profiles and language models into a single account structure, combining what would traditionally require separate accounts into one unified access point. This allows users to switch between different task environments (medical transcription, legal documentation, technical writing) while maintaining a single continuous session, greatly improving operational convenience
3Measurement precision
If the system loads different speech resources for each application, then resource accuracy improves, but the resource management complexity increases
Solution Approach 1:
The system implements feedback mechanisms that monitor application state, user behavior, and command patterns to automatically determine which language model and user profile should be active. This feedback loop allows the system to dynamically adjust resource allocation based on real-time conditions, ensuring high accuracy in resource matching while automating the management process to reduce complexity
Solution Approach 2:
The speech recognition system performs self-service by automatically selecting and switching between language models and user profiles based on contextual information from the running application and voice commands. The system manages its own resource allocation without requiring manual intervention from the user or complex external configuration, thereby achieving accurate resource matching while keeping management simple
Data Source
AI summary
The technology of the present application provides a method and apparatus to manage speech resources. The method includes detecting a change in a speech application that requires the use of different resources. On detection of the change, the method loads the different resources without the user needing to exit the currently executing speech application. The apparatus provides a switch (which could be a physical or virtual switch) that causes a speech recognition system to identify audio as either commands or text.