Personalized Voiceprint Model Training for Cross-State Recognition
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Solution Overview
Problem
Existing voiceprint recognition systems in electronic devices suffer from low accuracy due to variations in user voiceprint features caused by environmental, psychological, pathological, and age-related factors, leading to inconsistent recognition performance.
Innovation Solution
The electronic device trains a personalized voiceprint model based on the user's voice features in different states, updating it dynamically to improve recognition accuracy by incorporating voice samples in various emotional and speech speed conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a same preset voiceprint model is used in all electronic devices of a same type, then device complexity is reduced and ease of manufacture is improved, but voiceprint recognition accuracy deteriorates due to variations in user voiceprint features caused by environmental, psychological, pathological, and age-related factors
Solution Approach 1:
The system performs preliminary voiceprint model training during device initialization or setup phase, before actual voiceprint recognition tasks begin. The electronic device collects voice samples from the user during this preliminary stage and trains a personalized voiceprint model in advance, so that when recognition is needed, the model is already optimized for that specific user, resolving the contradiction between using a generic preset model and having a personalized accurate model.
2Reliability
If voiceprint features are extracted from voices in different states (emotions, speech speeds, environmental conditions), then recognition accuracy in varied conditions is improved, but the complexity of handling and processing diverse voice samples increases
Solution Approach 1:
The system dynamically adapts the voiceprint model to different user states by continuously collecting voice samples under varying conditions (different emotions, speech speeds, environmental noises) and updating the model accordingly. Instead of using a static model, the system adjusts the voiceprint features dynamically based on the user's current state, improving recognition reliability across diverse conditions while managing processing complexity through incremental updates.
3Measurement precision
If voiceprint recognition is performed without updating the model based on user variations, then processing speed is maintained and time consumption is reduced, but recognition accuracy deteriorates when user voiceprint features change over time
Solution Approach 1:
The system implements periodic voiceprint model updates at predetermined intervals (e.g., daily, weekly, or monthly) rather than continuous updates. During these periodic cycles, the electronic device collects new voice samples from the user and retrains or fine-tunes the voiceprint model. This periodic approach maintains recognition accuracy by adapting to user changes over time while avoiding excessive time consumption associated with continuous real-time model updates.
Data Source
AI summary
This application discloses a voiceprint recognition method, a graphical interface, and an electronic device. In the voiceprint recognition method, a voiceprint model is preset in the electronic device, and then the electronic device trains and updates the preset voiceprint model based on a voiceprint feature extracted from a voice of a registered user to obtain an exclusive voiceprint model belonging to the registered user. Finally, the electronic device uses the exclusive voiceprint model to generate a registered user representation based on the voiceprint feature of the voice of the registered user, and uses the registered user representation as a reference standard to realize voiceprint recognition on a voice of a speaker. Since the exclusive voiceprint model is trained based on voiceprint features of personal voices of the registered user, the registered user representation generated can accurately express voiceprint features of the user, thereby improving accuracy of voiceprint recognition.