Voice-Based Household User Identification Using Media Preferences
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Solution Overview
Problem
Current systems fail to accurately identify users within a household when no voice prints are stored for comparison, leading to inefficiencies in media consumption management.
Innovation Solution
A media guidance application generates a voice signature from user input, analyzes demographic characteristics, retrieves household user data, and uses media asset requests to identify the speaker by comparing demographic information and media preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice print comparison is used for user identification, then user identification accuracy is improved, but the system cannot identify users when no voice prints are stored
Solution Approach 1:
The system performs preliminary voice signature analysis to extract demographic characteristics before attempting user identification. This preliminary action enables the system to prepare identification criteria in advance, allowing it to function effectively even when no voice prints are stored for comparison.
Solution Approach 2:
The system introduces demographic characteristics as an intermediary element between voice input and user identification. By extracting and utilizing demographic information (age, gender, etc.) from the voice signature, the system creates a mediating identification method that works independently of pre-stored voice prints.
2Adaptability or versatility
If demographic analysis is added to voice recognition, then user identification capability is improved, but system complexity increases
Solution Approach 1:
The system merges voice signature analysis with demographic characteristic extraction into a unified identification process. By combining these functions, the system achieves enhanced user identification capability without proportionally increasing complexity, as the demographic analysis leverages existing voice processing infrastructure.
Solution Approach 2:
The voice signature analysis component serves multiple functions: it extracts both demographic characteristics and identification features. This multi-functionality reduces overall system complexity by eliminating the need for separate dedicated systems for demographic analysis and voice recognition.
3Reliability
If multiple identification methods are combined, then identification reliability is improved, but processing time increases
Solution Approach 1:
The system performs demographic characteristic extraction as a preliminary step that occurs simultaneously with voice signature generation. This preliminary action reduces subsequent processing time by preparing identification criteria in advance, rather than performing separate analysis steps sequentially.
Solution Approach 2:
The system maintains continuous processing by extracting demographic characteristics throughout the voice signature analysis process rather than adding separate discrete steps. This continuity ensures that identification reliability is improved through multiple data points without significant increases in total processing time.
Data Source
AI summary
Systems and methods are disclosed herein for identifying users based on voice data and media consumption data. A media guidance application may generate a voice signature from the user's input and based on that signature identify some demographic characteristics of the user (e.g., age, gender. and other suitable characteristics). The media guidance application may retrieve user data for users that are associated with a household of the user and attempt to identify which of the users spoke the command. If multiple users are identified, based on the demographic characteristics. the media guidance application may use the content of the voice command (e.g., a type of media requested) to identify the user.