Adaptive Speaker Recognition Thresholding
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Solution Overview
Problem
Current speaker recognition technologies face challenges in achieving high accuracy due to varying noise conditions, leading to high false rejection and false acceptance rates, which hampers their effectiveness in device login and authentication applications.
Innovation Solution
The implementation of adaptive thresholding in speaker recognition systems, which determines a dynamic threshold based on the speech-to-noise ratio, noise type, and target false accept/reject rates, using a pre-trained look-up table generated from diverse speech and noise scenarios, to optimize recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold is used for speaker recognition, then the system is simple to implement, but the false rejection and false acceptance rates increase under varying noise conditions
Solution Approach 1:
The patent implements adaptive thresholding where the speaker recognition threshold dynamically adjusts based on environmental noise conditions. The system calculates speech-to-noise ratio and uses pre-trained look-up tables to determine optimal thresholds for different noise scenarios, transforming the static threshold into a dynamic parameter that adapts to changing acoustic environments.
Solution Approach 2:
The system changes the threshold parameter based on noise characteristics. By analyzing speech-to-noise ratio and noise type, the system selects different threshold values from pre-trained models, effectively changing the recognition parameter to match environmental conditions and maintain high accuracy across varying scenarios.
2Measurement precision
If adaptive thresholding based on speech-to-noise ratio and noise type is implemented, then speaker recognition accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary training offline to create look-up tables that map noise conditions to optimal thresholds. During runtime, the system only needs to identify the current noise condition and retrieve the corresponding threshold from pre-computed tables, significantly reducing online computational complexity while maintaining high recognition precision.
Solution Approach 2:
The system uses pre-trained models and look-up tables as copies of optimal threshold configurations for different noise scenarios. Instead of computing optimal thresholds in real-time, the system copies appropriate threshold values from pre-trained data structures, reducing processing complexity while preserving measurement precision.
3Reliability
If the threshold is adjusted in real-time based on environmental noise, then false rejection and acceptance rates decrease, but the system requires more sophisticated noise analysis capabilities
Solution Approach 1:
The system segments noise analysis into distinct categories (e.g., different noise types and speech-to-noise ratio ranges). By dividing the continuous noise environment into discrete segments, the system can assign specific thresholds to each segment, simplifying the analysis complexity while maintaining reliable authentication across diverse acoustic conditions.
Data Source
AI summary
Techniques related to speaker recognition are discussed. Such techniques may include determining an adaptive speaker recognition threshold based on a speech to noise ratio and noise type label corresponding to received audio and performing speaker recognition based on the adaptive speaker recognition threshold and a speaker recognition score corresponding to received audio.