Speaker Recognition Quality Calibration via Enrollment Signal Assessment
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Solution Overview
Problem
Existing speaker recognition systems struggle to perform well in adverse and unrestricted conditions due to noise and variability in speech duration, especially in text-dependent speaker recognition.
Innovation Solution
A machine-learning architecture is developed to model quality measures for enrollment signals, allowing for the identification of deviations from expected signals and the generation of quality measures for audio descriptors. These quality measures are fused with speaker recognition embedding comparisons to calibrate outputs based on actual enrollment and testing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data augmentation is used to build more robust machine-learning architecture models, then the model's ability to handle noise improves, but the system still fails to generalize well to conditions with noise and duration of speech variability
Solution Approach 1:
The system performs preliminary quality assessment of enrollment signals before they are used for speaker verification. By evaluating quality measures (noise levels, duration, clarity) of enrollment recordings in advance, the system identifies and flags problematic enrollment data. This preliminary action prevents poor-quality enrollment signals from degrading the speaker verification model's performance, thereby improving both robustness to noise and generalization to variable conditions.
Solution Approach 2:
The system implements feedback by using quality measures of enrollment signals to adjust the speaker verification process. When enrollment signals are found to have poor quality (high noise, insufficient duration), the system generates alerts and can trigger re-enrollment. This feedback loop ensures that only adequate enrollment data is used, improving the system's ability to generalize across different noise and duration conditions while maintaining reliability.
2Measurement precision
If speaker recognition systems are trained with ideal enrollment signals, then verification accuracy improves, but the system fails to perform well in adverse and unrestricted conditions
Solution Approach 1:
The system changes the parameter of enrollment signal quality assessment by introducing quality measures (noise evaluation, duration checking, clarity metrics) that dynamically evaluate enrollment signals. Instead of assuming all enrollment signals are ideal, the system adjusts its verification threshold and confidence requirements based on the actual quality parameters of the enrollment data. This allows the system to maintain high verification accuracy for good-quality enrollments while adapting to adverse conditions when quality is poor.
Solution Approach 2:
The system performs preliminary quality assessment of enrollment signals before they are used for speaker verification. By evaluating quality measures (noise levels, duration, clarity) of enrollment recordings in advance, the system identifies and flags problematic enrollment data. This preliminary action prevents poor-quality enrollment signals from degrading the speaker verification model's performance, thereby improving both robustness to noise and generalization to variable conditions.
3Measurement precision
If the system evaluates quality measures for enrollment signals, then deviations from expected signals can be identified, but additional processing steps are required
Solution Approach 1:
The quality assessment module serves multiple functions: it evaluates noise levels, checks duration adequacy, assesses signal clarity, and generates quality scores that feed into the verification decision process. By making this single module multi-functional, the system achieves comprehensive quality measurement without proportionally increasing complexity. The same quality measures are used for both enrollment evaluation and ongoing verification monitoring, maximizing the utility of the added processing capability.
Data Source
AI summary
Embodiments described herein provide for a machine-learning architecture for modeling quality measures for enrollment signals. Modeling these enrollment signals enables the machine-learning architecture to identify deviations from expected or ideal enrollment signal in future test phase calls. These differences can be used to generate quality measures for the various audio descriptors or characteristics of audio signals. The quality measures can then be fused at the score-level with the speaker recognition's embedding comparisons for verifying the speaker. Fusing the quality measures with the similarity scoring essentially calibrates the speaker recognition's outputs based on the realities of what is actually expected for the enrolled caller and what was actually observed for the current inbound caller.