Voiceprint Recognition Model Construction via Keyword-Triggered Adaptive Training
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Solution Overview
Problem
Existing voiceprint recognition methods require users to dictate predetermined standard text for training, making the process cumbersome and less efficient, especially for text-independent recognition where no standard text can be used as a reference.
Innovation Solution
The system adapts by training a voiceprint recognition model using predetermined keywords relevant to specific application scenarios, allowing for adaptive training and updates without a traditional registration process, enabling voiceprint recognition during normal use and improving accuracy through continuous keyword detection and voice segment sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users dictate predetermined standard text for voiceprint training, then voiceprint recognition accuracy is improved, but user operation complexity and time consumption increase
Solution Approach 1:
The system pre-collects and stores multiple keywords related to specific application scenarios before the voiceprint training process. During training, the system automatically selects and uses these pre-prepared keywords as prompts, eliminating the need for users to manually prepare standard text while ensuring the training data is contextually relevant and accurate.
Solution Approach 2:
The system automatically extracts voice segments corresponding to detected keywords without requiring user intervention. The voiceprint training process is automated by detecting keywords in user input, extracting relevant voice segments, and using them for model training without manual text preparation or guidance from the user.
2Reliability
If traditional voiceprint registration process is used, then voiceprint model training is completed, but training time and user dedication are increased
Solution Approach 1:
The voiceprint training process is made dynamic and adaptive. The system continuously monitors user input for keywords, automatically extracts voice segments, and updates the voiceprint model in real-time or near-real-time. This dynamic approach replaces the static, time-consuming traditional registration process with an adaptive process that integrates seamlessly into normal system usage.
Solution Approach 2:
The system enables continuous voiceprint training by constantly monitoring user input for keywords and automatically extracting voice segments. Instead of a discrete, time-limited training session, the training process continues continuously as users interact with the system, accumulating useful voice data over time without requiring dedicated training periods.
3Ease of operation
If text-independent voiceprint recognition is implemented, then user convenience is improved, but modeling difficulty increases
Solution Approach 1:
The system segments user input into distinct voice segments based on detected keywords. Each keyword triggers extraction of a specific voice segment, which is then used for training. This segmentation approach simplifies the modeling process by breaking down continuous speech into discrete, manageable units associated with specific keywords, making text-independent recognition more tractable.
Solution Approach 2:
Keywords serve as intermediaries between the user's natural speech and the voiceprint training process. The system detects keywords in user input, uses them as mediators to identify and extract relevant voice segments, and then uses these segments for training. This intermediary approach bridges the gap between text-independent input and structured training requirements.
Data Source
AI summary
Technologies related to voiceprint recognition model construction are disclosed. In an implementation, a first voice input from a user is received. One or more predetermined keywords from the first voice input are detected. One or more voice segments corresponding to the one or more predetermined keywords are recorded. The voiceprint recognition model is trained based on the one or more voice segments. A second voice input is received from a user, and the user's identity is verified based on the second voice input using the voiceprint recognition model.