AI Voice Recognition Dynamic Parameter Adaptation
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Solution Overview
Problem
Existing AI devices face performance degradation when voice signals from environments different from the assumed use environment are input, due to fixed preprocessing parameters that do not adapt to varying noise and speech levels.
Innovation Solution
An AI device that includes a processor to acquire and preprocess voice signals, store their characteristics, and adjust preprocessing parameters based on the distribution of accumulated voice signal characteristics, thereby improving voice recognition performance across different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed preprocessing parameters are used for voice signals, then the voice recognition model shows superior performance when the voice signal matches the assumed use environment, but the voice recognition model shows inferior performance when the voice signal does not match the assumed use environment
Solution Approach 1:
The patent implements dynamic preprocessing parameters that automatically adjust based on the characteristics of incoming voice signals. The system calculates signal-to-noise ratio and other acoustic features in real-time, then adapts preprocessing parameters such as noise reduction strength and gain adjustment to match the current environment, resolving the contradiction between fixed parameters and environmental adaptability
Solution Approach 2:
The system changes preprocessing parameters based on the distribution of voice signal characteristics. By analyzing accumulated voice signal data and detecting changes in statistical distributions (mean, variance, etc.), the system dynamically adjusts parameters like normalization factors and filtering characteristics to optimize recognition performance across different environments
2Adaptability or versatility
If preprocessing parameters are adjusted to adapt to different environments, then voice recognition performance improves in varying environments, but the system complexity increases due to parameter distribution analysis and accumulation
Solution Approach 1:
The system performs self-adaptation by automatically analyzing its own performance and the characteristics of incoming signals. It accumulates voice signal characteristics locally, detects distribution changes, and adjusts parameters without external intervention, reducing the need for complex external control systems while maintaining high adaptability
Solution Approach 2:
The system implements feedback loops where recognition results and signal characteristics are continuously monitored. Based on this feedback, the system adjusts preprocessing parameters to optimize performance, creating a closed-loop control system that adapts automatically without requiring complex external management
Data Source
AI summary
An AI device is provided. The AI device includes a memory to store data, a voice acquisition interface to acquire a voice signal, and a processor to perform preprocessing for the voice signal based on a parameter, to provide the preprocessed voice signal to a voice recognition model, to acquire a voice recognition result, to store a characteristic of the preprocessed voice signal in the memory, and to change the parameter using a distribution of characteristics of voice signals accumulated in the memory.