Voice Print Authentication Using Device ID and Location
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Solution Overview
Problem
Current voice authentication systems for mobile devices lack robust user identification methods, relying on insecure methods like social security numbers or mother's maiden names, and fail to effectively utilize biometric characteristics for secure access.
Innovation Solution
A voice authentication method that receives spoken utterances, recognizes phrases, identifies biometric voice prints, and combines this with device identifiers to authenticate users, using variability in vocal tract configurations for unique identification and location verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional identification methods (social security numbers, mother's maiden names) are used for user authentication, then the system can operate with simple data storage and retrieval mechanisms, but the security and robustness of user identification deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/data-based identification methods (social security numbers, mother's maiden names) with biometric voice print authentication. The system captures voice samples, extracts acoustic features, and compares them against stored voice prints to authenticate users. This substitution of mechanical identification with biometric recognition significantly improves security while maintaining operational simplicity through automated voice pattern matching.
2Reliability
If voice authentication systems are implemented to improve user identification security, then authentication robustness is improved, but the complexity of the authentication system increases
Solution Approach 1:
The patent segments the voice authentication process into distinct functional modules: voice sample capture, acoustic feature extraction, voice print generation, and pattern matching comparison. By dividing the complex authentication task into separate processing stages, the system manages complexity through modular design while maintaining high authentication robustness through comprehensive voice biometric analysis.
Solution Approach 2:
The system transforms raw voice signals into standardized acoustic feature parameters (formant frequencies, spectral characteristics, temporal patterns) that can be systematically stored and compared. This parameter transformation approach converts complex voice data into manageable feature sets, enabling robust authentication while simplifying the underlying data handling through standardized parameter representations.
3Measurement precision
If biometric voice prints are used for user authentication, then user identification accuracy is improved, but the difficulty of detecting and measuring voice characteristics increases
Solution Approach 1:
The patent introduces acoustic feature extraction as an intermediary layer between raw voice signals and voice print storage. This intermediary process converts complex acoustic waveforms into standardized feature parameters (formants, spectral centroids, temporal characteristics) that are easier to measure, store, and compare. The feature extraction intermediary simplifies the measurement of voice characteristics while maintaining high identification accuracy through comprehensive feature analysis.
Data Source
AI summary
A method (700) and system (900) for authenticating a user is provided. The method can include receiving one or more spoken utterances from a user (702), recognizing a phrase corresponding to one or more spoken utterances (704), identifying a biometric voice print of the user from one or more spoken utterances of the phrase (706), determining a device identifier associated with the device (708), and authenticating the user based on the phrase, the biometric voice print, and the device identifier (710). A location of the handset or the user can be employed as criteria for granting access to one or more resources (712).