Language Identification via Comment Vector Aggregation
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Solution Overview
Problem
Existing video content identification systems face challenges in accurately determining the language of video content due to limitations in automatic speech recognition, especially with background music, noise, and multi-party conversations, leading to difficulties in serving users with content they can comprehend.
Innovation Solution
A method for language identification of media content items based on comments, where a subset of comments is selected and assigned language probability vectors, combined to generate a combined language vector, and used to identify the language of the media content, which can be augmented with metadata and other information to perform actions such as presenting related content or advertisements in the identified language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic speech recognition is used to determine video content language, then language identification can be performed, but accuracy deteriorates due to background music, noise, and multi-party conversations
Solution Approach 1:
The patent extracts language information from comments associated with video content instead of extracting speech from the video audio track itself. By taking out the language identification task from the problematic audio environment and relocating it to the text-based comment data, the system avoids the harmful effects of background music and noise while still achieving language identification.
2Device complexity
If metadata or user-provided information is used for language identification, then the process is simple, but reliability deteriorates due to incorrect or missing information
Solution Approach 1:
The patent uses multiple comments as feedback sources to determine video language. By aggregating language information from multiple independent comments and using voting mechanisms, the system achieves more reliable language identification compared to relying on a single metadata field or user-provided value. The feedback from multiple sources compensates for individual comment inaccuracies.
Solution Approach 2:
The patent makes the comment data serve multiple functions: it provides language identification, enables content filtering, and supports recommendation systems. By making comments a universal data source for multiple purposes, the system improves reliability without significantly increasing complexity, as the same data structure serves multiple analytical needs.
3Loss of information
If all comments are analyzed for language identification, then comprehensive data is obtained, but processing time and computational resources increase
Solution Approach 1:
The patent segments the comment analysis process by dividing comments into subsets based on relevance, recency, or other criteria. Instead of uniformly processing all comments, the system segments them into manageable groups and applies different processing strategies to each segment, reducing overall processing time while maintaining language identification accuracy.
Solution Approach 2:
The patent applies partial action by analyzing only a subset of comments necessary for confident language identification rather than processing every single comment. By using voting thresholds and stopping criteria, the system performs sufficient analysis to achieve reliable results without the excessive processing that would result from exhaustively analyzing all available comments.
Data Source
AI summary
Methods, systems, and media for language identification of a media content item based on comments are provided. In some embodiments, the method includes: obtaining a plurality of comments associated with a media content item; selecting a subset of the plurality of comments based on one or more criteria; assigning, for each comment in the subset of the plurality of comments, a vector of language probabilities, wherein each component of the vector is assigned a language probability that indicates the likelihood that the comment includes content in a language from a plurality of languages; combining the vector of language probabilities for each comment in the subset of the plurality of comments to generate a combined language vector; identifying a language associated with the media content item based on the combined language vector; and performing an action based on the identified language.