Dynamic Grammar Model Generation for Speech Recognition
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Solution Overview
Problem
Existing speech recognition systems face challenges in accurately recognizing speech due to the need for pre-established grammar models and pronunciation dictionaries, which can lead to misrecognition when dealing with devices that require specific command models and varying states.
Innovation Solution
A method and apparatus for generating a grammar model based on the state information of controllable devices, including information about operation states, controllability, and installation positions, to reduce misrecognition by dynamically updating the grammar model as device states change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed grammar model and pronunciation dictionary are used for speech recognition, then the system can operate with pre-established models, but misrecognition occurs when dealing with devices that require specific command models and varying states
Solution Approach 1:
The grammar model is transformed from a static, fixed structure to a dynamic one that automatically adapts to different device states. The system generates updated grammar models based on real-time device information such as operation modes, connected devices, and user preferences, enabling the speech recognition system to accurately recognize commands across varying contexts without requiring manual reconfiguration.
Solution Approach 2:
The system changes the parameters of the grammar model based on device state information. By modifying grammar model parameters according to detected device conditions (e.g., which devices are connected, current operation modes), the system achieves both high recognition accuracy and adaptability to different device configurations without compromising either aspect.
2Adaptability or versatility
If pre-established grammar models are used for multiple devices, then the system can support various devices, but the complexity of managing different command models increases
Solution Approach 1:
A single universal grammar model generation mechanism serves multiple device types and states. Instead of maintaining separate command models for each device, the system uses a unified approach where the grammar model is dynamically generated based on device information, reducing management complexity while supporting diverse devices.
Solution Approach 2:
The grammar model is segmented into reusable components that can be dynamically assembled based on device states. By dividing the grammar model into modular elements that can be selectively combined, the system manages complexity through organization while maintaining versatility across different device configurations.
3Reliability
If the grammar model is dynamically updated based on device states, then speech recognition accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining the framework and structure for grammar model generation, while only performing the specific customization based on device states when needed. This reduces the overall processing time by avoiding complete model regeneration and only computing the necessary variations based on current device conditions.
Data Source
AI summary
A method of obtaining a grammar model to perform speech recognition includes obtaining information about a state of at least one device, obtaining grammar model information about the at least one device based on the obtained information, and generating a grammar model to perform the speech recognition based on the obtained grammar model information.