Voice Modulation Matching for Familial Authentication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current authentication methods fail to effectively utilize voice modulation for familial relationship verification, which is indicative of genetic similarity, leading to potential security vulnerabilities and inefficiencies in accessing resources.
Innovation Solution
A system that captures and encodes digital audio streams of speech data using encryption algorithms, compares them with stored data to determine familial relationships, generates an authentication token, and records it on a distributed ledger, allowing authorized access to resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional identification credentials are used for authentication, then the authentication process is simple and quick, but security is compromised and familial relationships cannot be verified
Solution Approach 1:
The authentication system is segmented into multiple independent modules: voice capture module, DNA encryption module, similarity comparison module, and distributed ledger module. Each module handles a specific function, allowing the system to achieve high security through voice modulation analysis while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer that captures voice data, converts it to DNA-encoded representations, and compares it against stored familial patterns. This intermediary transformation of voice data into a comparable format enables secure familial verification without requiring direct complex comparisons of raw voice signals.
2Reliability
If voice modulation matching is implemented for familial verification, then authentication security is enhanced, but processing time and computational resources increase
Solution Approach 1:
Voice modulation patterns are pre-processed and encoded into DNA representations during enrollment, and these encoded patterns are stored in advance in the distributed ledger. When authentication is needed, the system only needs to perform a comparison of pre-encoded patterns rather than processing raw voice data from scratch, significantly reducing authentication time.
Solution Approach 2:
The system transforms voice data from the time-domain acoustic signal into a different parameter space using DNA encryption algorithms. This parameter transformation creates a compact, comparable representation that accelerates the similarity matching process while maintaining the unique characteristics needed for secure verification.
3Reliability
If speech data is encoded using DNA encryption algorithms, then data security is improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent replaces traditional cryptographic encoding mechanisms with a DNA-inspired encoding system. Instead of using conventional encryption algorithms, the system maps voice features into a DNA-like quadruple symbol system (A, C, G, T) with specific encoding rules, providing enhanced security through a fundamentally different approach that is more resistant to traditional cryptanalysis.
4Reliability
If distributed ledger technology is used to store authentication tokens, then data integrity and security are enhanced, but storage and processing overhead increases
Solution Approach 1:
The system extracts only the essential authentication elements (voice modulation patterns encoded as DNA sequences and corresponding authentication tokens) and stores them in the distributed ledger, rather than storing complete voice recordings or extensive user data. This selective extraction maintains data integrity and security while minimizing storage overhead.
Data Source
AI summary
Systems, computer program products, and methods are described herein for enhanced authentication using voice modulation matching. The present invention is configured to capture, via a first user input device, a digital audio stream of speech data of a first user; receive one or more identification credentials associated with the first user; encode the speech data to generate encoded speech data; query one or more data repositories using the encoded speech data; in response, retrieve, encoded speech data associated with a second user that matches the encoded speech data of the first user; determine that the first user has a familial relationship with the second user; generate an authentication token for the first user based on at least determining that the first user has a familial relationship with the second user; and record the authentication token for the first user in a distributed ledger associated with the second user.