Automated Speaker Authentication Testing via Synthetic Call Flows
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Solution Overview
Problem
Current voice biometric systems face challenges in accurately authenticating speakers due to noise and variability in voice patterns, leading to security and usability issues, and require manual testing which is inefficient and costly.
Innovation Solution
An automated system that uses software modules on a network-attached server to collect, analyze, and modify speech samples to create test scenarios, including ambient noise and network conditions, to improve speaker authentication accuracy by simulating various voice environments and user attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing is used to test speaker authentication system, then testing can be performed with existing infrastructure, but the process is inefficient and costly
Solution Approach 1:
The system automatically generates test scripts, selects test speakers, executes authentication tests, and analyzes results without requiring manual intervention. The testing system serves itself by autonomously managing the entire test lifecycle from script generation to result analysis, eliminating the need for manual testing operations.
Solution Approach 2:
The system creates virtual copies of real authentication scenarios by generating synthetic test scripts that replicate real-world authentication conditions. These virtual test scenarios can be repeatedly executed without consuming additional physical resources, allowing parallel testing of multiple authentication conditions simultaneously.
2Reliability
If voice authentication accuracy is increased by using stricter thresholds, then security against impostors improves, but legitimate speakers may be rejected (false rejects)
Solution Approach 1:
The system automatically analyzes authentication results and provides feedback on false accepts and false rejects. By reviewing test outcomes, the system identifies threshold settings that create security-usability tradeoffs and recommends optimizations to balance both requirements without manual intervention.
Solution Approach 2:
The system automatically adjusts authentication threshold parameters based on test results and statistical analysis. By dynamically optimizing these parameters, the system achieves the highest possible security while minimizing false rejects of legitimate speakers.
3Reliability
If more comprehensive testing scenarios are created to cover all voice conditions, then authentication reliability improves, but the complexity and cost of testing increases
Solution Approach 1:
The testing system performs multiple functions through a single integrated platform: generating test scripts, selecting test speakers, executing authentication tests, analyzing results, and providing recommendations. This multi-functional approach consolidates what would otherwise require multiple separate testing tools and manual processes into one unified system.
Solution Approach 2:
The system automatically varies testing parameters such as noise levels, accent types, and authentication conditions to create comprehensive test scenarios. By programmatically changing these parameters rather than manually configuring each condition, the system achieves thorough coverage without proportionally increasing operational complexity.
Data Source
AI summary
A system for automated adaptation and improvement of speaker authentication in a voice biometric system environment, comprising a speech sample collector, a target selector, a voice analyzer, a voice data modifier, and a call flow creator. The speech sample collector retrieves speech samples from a database of enrolled participants in a speaker authentication system. The target selector selects target users that will be used to test the speaker authentication system. The voice analyzer extracts a speech component data set from each of the speech samples. The call flow creator creates a plurality of call flows for testing the speaker authentication system, each call flow being either an impostor call flow or a legitimate call flow. The call flows created by the call flow creator are used to test the speaker authentication system.