Speech User Recognition Across Multi-Source Confidence Thresholds
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Solution Overview
Problem
Existing speech processing systems face challenges in managing varying user recognition confidence thresholds across different content sources, leading to undesirable operations and potential data disclosure to unauthorized users when a single utterance invokes multiple content sources with different verification requirements.
Innovation Solution
A system that groups content sources and performs user recognition with the most stringent confidence threshold satisfied, ensuring personalized responses only if the highest user recognition confidence is met, thereby reducing unintended data disclosure and maintaining system integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system applies multiple content sources with different user recognition confidence thresholds to process a single utterance, then the system can provide more comprehensive and personalized content, but the risk of unauthorized data disclosure increases and user friction increases
Solution Approach 1:
The system segments content sources into different groups based on their user recognition confidence thresholds. Each group has a specific threshold level, and the system selectively applies content sources from appropriate groups depending on the verified user identity and the sensitivity level of the requested content. This segmentation allows the system to provide personalized content while maintaining security by not exposing all content sources to all users.
Solution Approach 2:
The system applies different quality levels of content source verification to different content requests based on local needs. High-sensitivity content requires higher confidence thresholds and more stringent verification, while less sensitive content can use lower thresholds. This local quality approach ensures that each content access decision is tailored to the specific security requirements of that content, balancing personalization with security.
2Adaptability or versatility
If the system applies multiple content sources with different user recognition confidence thresholds to process a single utterance, then the system can provide more comprehensive and personalized content, but user friction increases due to multiple verification requirements
Solution Approach 1:
The system performs preliminary grouping of content sources by confidence threshold levels before user interaction occurs. This pre-organization allows the system to quickly determine which content sources are appropriate for a given user verification level without requiring multiple sequential verification steps from the user. The user provides a single utterance, and the system internally manages the complexity of multiple content sources.
Solution Approach 2:
The system creates a universal verification mechanism that handles multiple content sources with different thresholds through a single unified process. Instead of requiring separate verification procedures for each content source, the system uses one multi-functional verification approach that adapts to the appropriate threshold group based on the user's verified identity and the content sensitivity, reducing user friction while maintaining comprehensive content access control.
3Reliability
If the system satisfies only the most stringent confidence threshold for user recognition, then data security is improved, but the ability to access content from sources with lower thresholds is reduced
Solution Approach 1:
The system dynamically adjusts which content source groups are accessible based on the verified user identity and the specific content request. Rather than statically applying the most stringent threshold to all content, the system flexibly matches users to appropriate content source groups. A user verified at a high confidence level can access both high-security and lower-security content, while the system optimizes by only requiring the necessary verification level for each specific access request.
Solution Approach 2:
The system adds a dimensional layer to content access control by organizing content sources not just by security level but by multiple threshold groups simultaneously. This multi-dimensional organization allows the system to satisfy the most stringent threshold requirement for security while still providing access to content from lower-threshold groups through a single verification process. The additional dimension of threshold grouping enables nuanced access control that maintains security without overly restricting content availability.
Data Source
AI summary
Systems, methods, and devices for recognizing a user are disclosed. A speech-controlled device captures a spoken utterance, and sends audio data corresponding thereto to a server. The server determines content sources storing or having access to content responsive to the spoken utterance. The server also determines multiple users associated with a profile of the speech-controlled device. Using the audio data, the server may determine user recognition data with respect to each user indicated in the speech-controlled device's profile. The server may also receive user recognition confidence threshold data from each of the content sources. The server may determine user recognition data associated that satisfies (i.e., meets or exceeds) a most stringent (i.e., highest) of the user recognition confidence threshold data. Thereafter, the server may send data indicating a user associated with the user recognition data to all of the content sources.