Voice Query Acoustic Features for Age-Aware Content Ranking
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Solution Overview
Problem
Current NLP and voice recognition systems fail to consider user attributes such as age and content appropriateness when ranking and presenting search results, leading to irrelevant content being provided to users, particularly children and adults.
Innovation Solution
Leverage acoustic features to determine user demographics and adjust relevance scores of content items based on entity types (child, adult, or unknown) to ensure appropriate content is presented, using techniques like automatic speech recognition and natural language processing to tailor search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP and voice recognition systems use context from user's voice query to match entity types, then search accuracy is improved, but content appropriateness for different user demographics deteriorates
Solution Approach 1:
The system applies different quality filters to search results based on the detected demographic characteristics of the user. Acoustic features of the voice query are analyzed to determine user attributes such as age group, and then content appropriateness is adjusted locally for each user segment. This allows the same search query to return different ranked results depending on the user's demographic profile, resolving the contradiction between maintaining search accuracy and ensuring content appropriateness across different demographics.
2Measurement precision
If content items are ranked based on entity type weights from ontology-based knowledge system, then search relevance is improved, but user personalization deteriorates
Solution Approach 1:
The ranking system transitions from a static ontology-based approach to a dynamic personalized ranking mechanism. While entity type weights from the ontology provide the baseline relevance scoring, the system dynamically adjusts these rankings by incorporating acoustic feature analysis of the user's voice query. This allows the search results to be both relevant to the query and personalized to the user's demographic characteristics, resolving the contradiction between search relevance and user personalization.
3Loss of information
If voice queries are processed using standard speech recognition, then query understanding is improved, but demographic identification capability deteriorates
Solution Approach 1:
The system merges standard speech recognition processing with acoustic feature analysis to simultaneously achieve query understanding and demographic identification. While the speech recognition component transcribes and understands the semantic meaning of the voice query, a parallel acoustic analysis component extracts spectral characteristics and other voice features that indicate user demographics. This combined approach resolves the contradiction by showing that the same acoustic information can serve dual purposes: understanding the query and identifying the user's demographic profile.
Data Source
AI summary
The methods and systems described herein leveraging acoustic features of a user to generate and present a personalized content to a user. In one example, the method receives a voice query and determines that the query refers to either a first content item or a second content item. The first content item is associated with a first type assigned with a first score and the second content item is associated with a second entity type assigned with a second score. The method also determines whether the query is from the second entity type. The method ranks the first and the second content items based on this determination and generates for presentation of the first and the second content items based on the ranking. The method also changes the first or the second scores based on this determination and selects one of the first or the second content item for presentation.