Vehicle Voiceprint Authentication With Preference Embedding Interpolation
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Solution Overview
Problem
Existing vehicle authentication methods, such as passwords and PIN codes, are easily compromised and inconvenient, while current voice biometric systems lack robustness and flexibility for secure and convenient user authentication.
Innovation Solution
A voiceprint authentication system using neural networks to generate and compare voice features, with a probabilistic notion for interpolation and user preference embedding, enabling secure and flexible user authentication in vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods (passwords, PIN codes) are used, then implementation is simple, but security is compromised and convenience is reduced
Solution Approach 1:
The patent replaces traditional mechanical authentication systems (passwords, PIN codes, physical keys) with a voice biometric system that uses acoustic signals and neural network processing. The voiceprint authentication system captures voice signals, extracts biometric features, and compares them against stored templates using neural networks, thereby substituting mechanical credential verification with biological characteristic recognition.
Solution Approach 2:
The system transforms the authentication parameter from discrete credentials (passwords, PINs) to continuous biometric signals (voice characteristics). By changing the authentication parameter to voice frequency, timbre, and spectral features, the system achieves higher security while maintaining user convenience through natural voice interaction.
2Ease of operation
If voice biometric authentication is implemented, then convenience and security are improved, but robustness against variability and interpolation accuracy deteriorate
Solution Approach 1:
The system performs preliminary enrollment where voice samples are collected and processed to create standardized voiceprint templates before actual authentication occurs. During enrollment, the neural network pre-processes voice signals to extract stable biometric features and stores them as reference templates, enabling rapid and robust authentication later without requiring complex real-time processing.
Solution Approach 2:
The patent introduces voiceprint templates as intermediary representations between raw voice signals and authentication decisions. These templates serve as mediators that capture essential biometric characteristics while filtering out irrelevant variability, enabling robust comparison during authentication without directly processing variable live voice signals.
3Ease of operation
If voiceprint authentication is used, then user convenience is improved, but accuracy under varying conditions deteriorates
Solution Approach 1:
The system transforms voice signals from raw acoustic waveforms to standardized spectral and temporal feature representations. By changing parameters to frequency-domain features (MFCCs, spectrograms) and normalizing for pitch and volume variations, the system maintains measurement precision across different speaking conditions while preserving user convenience.
Solution Approach 2:
The patent applies preprocessing techniques that cushion against expected variations before authentication. This includes noise filtering, voice activity detection, pitch normalization, and artifact removal that prepare voice signals in advance, ensuring accurate similarity measurement even under varying acoustic conditions.
Data Source
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AI summary
Embodiments are direct to methods and systems for authenticating a user and interpolating user preference embeddings. The systems generate, using a neural network trained to generate features based on training data comprising human voices spoken by a plurality of historical speakers inside a vehicle, input features based on a human voice of a current speaker inside the vehicle, and calculates similarities between an input vector of the input features and historical vectors in voiceprints of one or more enrolled users. After determining a similarity between the input vector and at least one historical vector in a voiceprint of an identified user is less than a threshold similarity, the systems authenticate the current speaker as the identified user, calculate a probabilistic notion based on the similarity, and apply the probabilistic notion to interpolate between downstream user preference embeddings associated with the identified user.