Acoustic Aware Voice User Interface Context Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Voice user interfaces (VUIs) experience low accuracy in challenging noise environments due to low signal-to-noise ratios, leading to suboptimal performance in diverse acoustic settings.
Innovation Solution
A context awareness module (CAM) is used to adaptively adjust VUI algorithms by providing information on acoustic environments, allowing the system to switch between statistical models and computing resources for optimal performance, such as activating high complexity algorithms on cloud platforms in low SNR conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high complexity VUI algorithms are used to improve voice recognition accuracy, then detection accuracy improves, but device complexity and computational resource requirements increase
Solution Approach 1:
The system dynamically adapts VUI algorithm complexity based on real-time acoustic environment assessment. The context awareness module evaluates acoustic conditions and adjusts the complexity of processing algorithms accordingly, using higher complexity algorithms only when acoustic conditions warrant the improved accuracy they provide.
Solution Approach 2:
The system changes operational parameters (algorithm complexity level) based on environmental conditions. By monitoring acoustic environment parameters and adjusting algorithm complexity in response, the system optimizes the balance between recognition accuracy and computational resource consumption.
2Measurement precision
If high complexity VUI algorithms are activated to improve accuracy in low SNR conditions, then voice detection accuracy improves, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts energy consumption by adapting algorithm complexity to acoustic conditions. In challenging acoustic environments, the system accepts higher energy consumption in exchange for improved detection accuracy, while in favorable conditions it reduces energy usage by employing simpler algorithms.
Solution Approach 2:
The system changes the operational parameter of algorithm complexity based on acoustic environment assessment, thereby controlling energy consumption. The context awareness module enables the system to optimize the trade-off between detection accuracy and energy usage by selecting appropriate processing complexity levels.
3Adaptability or versatility
If adaptive adjustment of VUI algorithms based on acoustic context is implemented, then voice recognition accuracy in diverse environments improves, but device complexity increases
Solution Approach 1:
The system segments the VUI processing into distinct functional modules: an acoustic environment assessment module that evaluates context, and a VUI processing module that executes appropriate algorithms. This segmentation allows independent optimization of each module and simplifies the overall system architecture.
Solution Approach 2:
The context awareness module serves as an intermediary between the acoustic environment and the VUI processing algorithms. It assesses environmental conditions and mediates the selection of appropriate processing complexity levels, enabling adaptive performance without requiring the entire system to be maximally complex.
Data Source
AI summary
A method for optimal configuration of a voice user interface is disclosed herein. The method includes receiving an audio signal; processing the audio signal by a context awareness module to generate context information regarding an acoustic environment of the audio signal; determining, based on the context information, an optimal one of a plurality of different configurations of a voice user interface to perform voice user interface processing of the audio signal; and performing the voice user interface processing of the audio signal using the optimal configuration of the plurality of different configurations.