Voice Changer System with Transparency Feedback
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Solution Overview
Problem
Users' privacy is compromised during voice calls due to potential malware, phishing, and unauthorized AI model training using their speech, necessitating a solution to disguise their voice and prevent nefarious use of voice alteration services.
Innovation Solution
A system that includes a server and client devices configured to detect privacy risks and automatically change a user's voice during calls by analyzing context, connection characteristics, and user preferences, using a speech recognizer and synthesizer to generate speech in a selected voice, while providing authentication to ensure transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice alteration services are used to disguise user voice during calls, then user privacy is protected and unauthorized voice sampling is prevented, but the authenticity and transparency of communication is compromised
Solution Approach 1:
The system provides feedback to the receiving device about whether voice alteration is being applied. The receiving device can determine if the incoming audio has been processed and can notify the user, maintaining transparency while allowing privacy protection to function.
Solution Approach 2:
The server acts as an intermediary between the calling and receiving devices, implementing voice alteration on the server side rather than on the client devices. This centralizes the privacy protection function while maintaining communication flow.
2Reliability
If voice synthesizer processes audio in real-time during calls, then voice disguise is achieved, but processing delay increases
Solution Approach 1:
The system pre-loads voice models and synthesizer components before they are needed during a call. This preparation work is done in advance so that when voice alteration is required during the call, the processing can begin immediately with minimal delay.
Solution Approach 2:
The voice synthesis and processing functions are extracted from the client devices and placed on the server. This allows for more powerful processing resources to be dedicated to the task without impacting the performance or battery life of mobile devices.
3Measurement precision
If comprehensive context analysis is performed to detect privacy risks, then accuracy of privacy protection is improved, but computational complexity and processing time increase
Solution Approach 1:
The context analysis is segmented into multiple independent factors: connection characteristics (encrypted vs. unencrypted), device information (AI capabilities, processor type), location data (country, state, city), and call metadata (duration, time of day). Each factor can be evaluated separately and combined to determine overall privacy risk.
Solution Approach 2:
Different levels of analysis are applied to different aspects of the context. Critical factors like encryption status receive immediate binary assessment, while less critical factors like location are analyzed with varying degrees of detail based on the overall risk profile being assembled.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identity management are disclosed. In one aspect, a method includes the actions of receiving, from a first computing device, first audio data that includes representations of one or more words in a first voice. The actions further include generating second audio data that includes representations of the one or more words in a second voice. The actions further include providing, for output to a second computing device, the second audio data.