Speaker Authentication Feedback Using Calendar Data
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Solution Overview
Problem
Conventional electronic devices using speaker authentication lack the ability to provide meaningful feedback to users regarding the suitability of speech data for training and authentication, leading to potential misinterpretation of the feature's performance and reliability.
Innovation Solution
The device extracts side information from speech and environmental data to determine suitability for speaker model training and generates context-specific feedback messages, ensuring accurate expectations and user experience improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional devices use invariable greeting/fail messages for speaker authentication, then the device complexity is low, but the user experience and information quality deteriorate
Solution Approach 1:
The patent implements a feedback mechanism that analyzes side information (environmental conditions, speech quality metrics, calendar data) and generates contextualized feedback messages. This feedback loop provides users with meaningful information about authentication outcomes and reasons, transforming the simple invariable message system into an intelligent communication system that adapts to different situations while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces side information extraction and analysis as an intermediary layer between the speaker authentication process and the feedback message generation. This intermediary component processes environmental data, speech characteristics, and calendar information to create contextualized feedback, thereby improving information quality without requiring complete system redesign and keeping the overall complexity controlled through specialized modular components.
2Ease of operation
If speaker authentication provides only binary outcomes (access granted/denied), then the system is simple to operate, but user understanding and trust in the feature deteriorate
Solution Approach 1:
The patent enhances the binary authentication outcome with contextualized feedback messages that explain the reasons behind access decisions. By analyzing side information such as environmental noise levels, speech quality metrics, and calendar context, the system provides users with understandable explanations (e.g., 'authentication failed due to background noise' or 'training data rejected because of unusual speaking conditions'), thereby maintaining operational simplicity while building user trust through transparency.
3Loss of information
If the device extracts and analyzes side information from speech and environmental data, then the quality of feedback messages improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs side information extraction and analysis in advance during the speech collection and authentication preparation phases. By pre-processing environmental data, speech characteristics, and calendar information before the actual authentication decision, the system prepares contextualized feedback messages ahead of time, thereby reducing real-time processing delays while maintaining high feedback quality.
Solution Approach 2:
The patent implements selective side information processing based on authentication context and confidence levels. Not all side information is processed equally - the system focuses computational resources on the most relevant factors (e.g., environmental noise during authentication, speech quality during training) and uses heuristic rules to determine when full analysis is necessary versus when simplified processing suffices, thereby optimizing the balance between feedback quality and processing time.
Data Source
AI summary
An embodiment of the invention provides a method of preparing for speaker authentication. The method includes: receiving speech data that represents an utterance made by a user; extracting side information; examining the side information to determine whether to allow speaker model training using the speech data; and generating a feedback message for the user based on the side information if speaker model training using the speech data is not allowed; wherein the feedback message contains a message indicating at least a condition of the side information comprising calendar data to the user.