Speaker Recognition System Using Dual-Module Segmentation
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Solution Overview
Problem
Current speaker authentication systems suffer from low accuracy and lack customizable settings, making them unsuitable for diverse environments and potentially insecure in critical applications.
Innovation Solution
A speech-based speaker recognition system that correlates feature vectors over time to match reference and test speech inputs, using passphrase recognition and voice feature analysis to authenticate speakers based on both spoken phrases and voice characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current speaker authentication systems are used, then the system is simple to operate, but the authentication accuracy is unacceptably low
Solution Approach 1:
The authentication system is divided into two independent modules: passphrase recognition module and voice feature recognition module. Each module processes a different aspect of speech authentication, and both must agree for successful authentication. This segmentation allows each module to be optimized independently while achieving high overall accuracy through combined decision-making.
Solution Approach 2:
The system merges passphrase recognition (what is said) with voice feature recognition (how it is said) into a unified authentication decision. The passphrase recognition module verifies the content matches the reference, while the voice feature recognition module verifies the speaker identity matches, and both results are combined to make the final authentication determination.
2Adaptability or versatility
If current speaker authentication systems are used, then the system structure is simple, but the system lacks customizable settings for authentication stringency
Solution Approach 1:
The system incorporates adjustable authentication thresholds and configurable decision rules that can be dynamically modified based on security requirements. Administrators can adjust the stringency of passphrase matching, voice feature matching, and the weighting of each module's contribution to the final decision, allowing the system to adapt to different security levels and application scenarios.
3Reliability
If speech recognition technology is used for speaker authentication, then the system can authenticate speakers using speech content, but the accuracy is insufficient for dangerous environments
Solution Approach 1:
The system separates speaker identification into two distinct tasks: verifying what was said (passphrase recognition) and verifying who said it (voice feature recognition). This segmentation ensures that neither task can compensate for failures in the other, thereby improving reliability for high-stakes applications where both aspects must be correctly authenticated.
Solution Approach 2:
The system provides detailed feedback from both the passphrase recognition module and the voice feature recognition module, allowing administrators to understand which aspect of authentication failed and adjust thresholds or configurations accordingly to improve overall reliability in critical environments.
Data Source
AI summary
The illustrative embodiments described herein provide systems and methods for authenticating a speaker. In one embodiment, a method includes receiving reference speech input including a reference passphrase to form a reference recording, and receiving test speech input including a test passphrase to form a test recording. The method includes determining whether the test passphrase matches the reference passphrase, and determining whether one or more voice features of the speaker of the test passphrase matches one or more voice features of the speaker of the reference passphrase. The method authenticates the speaker of the test speech input in response to determining that the reference passphrase matches the test passphrase and that one or more voice features of the speaker of the test passphrase matches one or more voice features of the speaker of the reference passphrase.