Voice Recognition Noise Category Adaptation
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Solution Overview
Problem
Current voice recognition systems in electronic devices face challenges in accurately recognizing voice commands in varying environments due to background noise, leading to reduced performance and reliability.
Innovation Solution
The electronic device employs a processor to determine the noise category of background noise signals and selects or generates a corresponding voice model to improve voice recognition, allowing it to recognize voice signals effectively across different environments by separating voice signals from background noise and adapting to noise categories specific to each environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single voice model is used for voice recognition, then the device complexity is reduced, but the voice recognition accuracy deteriorates in varying noise environments
Solution Approach 1:
The system dynamically selects voice models based on detected noise categories. Instead of using a fixed single model, the system adapts by choosing from multiple pre-trained voice models corresponding to different noise environments (e.g., quiet, noisy, windy conditions), thereby maintaining high recognition accuracy across varying conditions without permanently increasing hardware complexity
Solution Approach 2:
The system changes the parameter of voice model selection based on noise category detection. By detecting the current noise environment and selecting the appropriate pre-trained model, the system optimizes recognition accuracy for each condition without requiring a completely new system architecture, thus managing complexity while improving performance
2Reliability
If multiple voice models are prepared for different noise categories, then the voice recognition accuracy in varying environments is improved, but the device complexity increases
Solution Approach 1:
Multiple voice models are pre-trained offline for different noise categories before deployment. The system prepares these models in advance and stores them, so that during operation it only needs to detect the noise category and select the appropriate pre-prepared model, rather than adapting or training models in real-time, thus improving reliability without excessive runtime complexity
Solution Approach 2:
A single voice recognition system is designed to handle multiple noise environments by incorporating multiple pre-trained models. The system achieves multi-functionality by being able to operate effectively in various noise conditions (quiet, noisy, windy, etc.) through model selection, rather than requiring separate dedicated systems for each condition
3Adaptability or versatility
If noise category determination is performed, then the adaptability to different environments is improved, but the processing time increases
Solution Approach 1:
The noise detection process is segmented into distinct noise categories (e.g., quiet, noisy, windy conditions). By dividing the continuous noise spectrum into discrete categories, the system can quickly classify the current environment and select the corresponding pre-trained model, reducing the time required for adaptation compared to continuous analysis
Data Source
AI summary
Provided is an electronic device and a voice recognition method. The electronic device includes: a memory configured to store at least one instruction, and a processor electrically connected to the memory. The processor is and configured to execute the at least one instruction to: obtain a sound signal corresponding to an utterance, and recognize a voice signal included in the sound signal, based on a determination that a portion of the sound signal corresponds to at least one of a plurality of noise categories, the plurality of noise categories corresponding to a plurality of environments in which a plurality of voice models of voice signals are generated.