Location-Based Context Speech Recognition Vocabulary Segmentation
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Solution Overview
Problem
Existing mobile speech recognition systems face challenges in reducing computational resources and adapting to changing scenarios due to limited processing power and bandwidth constraints, especially in small form factor devices, leading to reduced functionality and ineffective interaction with users.
Innovation Solution
Implementing a location-based context-driven speech recognition system that uses a mobile device's position locator to determine a vocabulary subset from a universal vocabulary, allowing the speech recognizer to process voice commands more accurately and efficiently by limiting expected commands based on the device's context, such as proximity features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large database of recognizable vocabularies is used to address all possible speech scenarios, then speech recognition accuracy is improved, but computing resources required increase significantly
Solution Approach 1:
The patent segments the universal vocabulary database into location-specific vocabulary subsets. The speech recognition system divides the large vocabulary into multiple smaller subsets based on geographic locations, allowing the device to process only the relevant subset corresponding to the current location, thereby reducing computational resources while maintaining recognition accuracy for contextually relevant commands
Solution Approach 2:
The patent applies local quality by making the vocabulary database location-dependent. Each location has its own optimized vocabulary subset tailored to the local context and expected commands. This allows the system to use a smaller, more efficient vocabulary locally rather than processing the entire universal vocabulary, reducing power consumption while maintaining accuracy for location-specific speech recognition
2Measurement precision
If speech recognition data is sent to a central cloud-based system for processing, then speech recognition capability is improved, but network bandwidth dependency increases
Solution Approach 1:
The patent extracts the essential speech recognition processing capability from the cloud-based system and implements it locally on the mobile device. By storing location-specific vocabulary subsets locally and performing speech recognition processing on-device, the system eliminates the need for continuous network communication, reducing bandwidth dependency while maintaining speech recognition functionality
Solution Approach 2:
The patent enables the mobile device to perform speech recognition independently using locally stored vocabulary subsets. The device serves its own speech recognition needs without requiring external cloud processing, allowing it to operate autonomously even when network bandwidth is limited or unavailable, thereby reducing dependency on network infrastructure
3Productivity
If task-specific recognizable vocabularies are used to reduce processing power requirements, then device functionality is improved, but adaptability to new scenarios decreases
Solution Approach 1:
The patent makes the vocabulary subset dynamic by associating it with geographic locations. As the user moves to different locations, the system automatically switches to the appropriate vocabulary subset for that location. This dynamic adaptation allows the device to maintain high processing efficiency with location-specific vocabularies while simultaneously adapting to new scenarios and environments based on the user's current position
Data Source
AI summary
Systems, methods, and devices for location-based context driven speech recognition are disclosed. A mobile or stationary computing device can include position locating functionality for determining the particular physical location of the computing device. Once the physical location of the computing device determined, a context related to that particular physical location. The context related to the particular physical location can include information regarding objects or experiences a user might encounter while in that particular physical location. The context can then be used to determine delimited or constrained speech recognition vocabulary subset based on the range of experiences a user might encounter within a particular context. The speech recognition vocabulary subset can then be referenced or used by a speech recognizer to increase the speed, accuracy, and effectiveness in receiving, recognizing, and acting in response to voice commands received from the user while in that particular physical location.