Context-Aware Voice Command Selection for Noisy Environments
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Solution Overview
Problem
Voice command interfaces in devices like smartphones and AR/VR goggles face challenges due to similar-sounding commands and difficulty in detecting wake words in noisy environments, leading to impaired user experience and slowed adoption.
Innovation Solution
A device and server system that selects voice commands or wake words based on context information and user profiles with adjustable context constraint weights, improving detection accuracy by considering environmental noise and user activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice command interfaces are implemented in devices, then user interaction capabilities are enhanced, but accuracy of command recognition deteriorates due to similar-sounding commands and environmental noise
Solution Approach 1:
The system performs preliminary actions by determining context information about the user and environment before processing the voice command. This includes identifying user activities, device states, and environmental conditions in advance to establish context constraints that will guide accurate command recognition later
Solution Approach 2:
The system uses feedback mechanisms by comparing the recognized voice command against context constraints and user profiles. The context information about user activities and environmental noise provides feedback that helps distinguish between similar-sounding commands and improves recognition accuracy
2Measurement precision
If context information processing is added to improve voice command accuracy, then command recognition precision improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of voice command recognition into distinct components: determining context information (user activities, device states, environmental conditions), establishing context constraints, and then processing the voice command against these constraints. This segmentation manages complexity by breaking down the overall system into manageable parts
Solution Approach 2:
The system changes parameters by adjusting the weight of different context constraints based on user profiles and current situations. By dynamically modifying constraint weights rather than using fixed thresholds, the system adapts to different contexts without requiring complex hard-coded rules for every scenario
Data Source
AI summary
A user equipment (UE), a server, or a combination of a UE and server configured to select a voice command or a wake word decision based on voice input received for the UE, on context information associated with the UE, and on a user profile associating voice commands or wake word decisions with context constraint weights is described herein. The UE, server, or combination also receives the voice input, determines context information related to an activity of the UE, a characteristic of the UE, a location of the UE, or a connection of the UE, and either executes the selected voice command or acts on the wake word decision. As server of the wireless network, connected to the UE, may also be configured to generate default user profiles and provide the UE with one of the default user profiles.