Noise Sample Comparison for Voice Recognition Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Voice recognition systems often fail to recognize voice commands in environments with ambient or background noise, leading to unsuccessful or incorrect actions.
Innovation Solution
A device records a noise sample in the environment, compares it to predetermined thresholds or noise samples, and determines the likelihood of successful voice recognition, triggering a notification to the user indicating the likelihood of successful recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice recognition is used in noisy environments, then hands-free interaction is enabled, but recognition accuracy deteriorates
Solution Approach 1:
The system performs preliminary noise assessment by recording and analyzing environmental noise samples before initiating voice recognition. It compares the recorded noise against predetermined thresholds to determine if the environment is suitable for voice recognition, and only then allows the voice recognition function to proceed, preventing poor recognition attempts in advance.
Solution Approach 2:
The system provides feedback to the user about the likelihood of successful voice recognition by displaying notifications that indicate whether the environment is suitable for voice commands. This feedback loop allows users to understand the current recognition reliability and adjust their behavior accordingly, such as moving to a quieter location or using alternative input methods.
2Reliability
If noise assessment is performed before voice recognition, then recognition reliability is improved, but system complexity increases
Solution Approach 1:
The system performs only essential noise assessment functions - recording ambient noise and comparing it against predetermined thresholds - rather than implementing a complete acoustic analysis system. This partial action approach provides sufficient reliability improvement without requiring complex signal processing algorithms or extensive computational resources.
Solution Approach 2:
The system uses simple, easily implementable noise assessment mechanisms that do not require expensive hardware or complex software. The noise sampling and threshold comparison can be implemented using basic audio recording capabilities already present in most devices, avoiding the need for specialized microphones or advanced acoustic processing systems.
Data Source
AI summary
Methods and devices are disclosed for notifying a user of a likelihood of successful recognition in an environment by a voice recognition application. In one embodiment, the method includes a device recording a noise sample in an environment and making a comparison of the noise sample and at least one predetermined threshold. The method further includes, based on the comparison, determining a likelihood of successful recognition in the environment by a voice recognition application, and triggering a notification indicating the likelihood. In another embodiment, the device includes a microphone configured to record a noise sample in an environment, a processor, and data storage comprising instructions executable by the processor to make a comparison of the noise sample and at least one predetermined threshold, based on the comparison, determine a likelihood of successful recognition by a voice recognition application, and trigger a notification indicating the likelihood.