Closed-Caption Text Timeline Alignment via N-Gram Analysis
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Solution Overview
Problem
Existing methods for aligning audio transcripts with video timelines in multimedia files are inaccurate, leading to time lags and errors in metadata indexing and playback, particularly due to the limitations of human-generated closed captions and automated speech recognition software.
Innovation Solution
The method employs N-gram analysis to synchronize closed-captioned text with ASR-generated text, using a comparison process that aligns each word of the closed-captioned text with the corresponding point in time based on the ASR text's timeline accuracy, creating a time-based metadata file for precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-generated closed captions are used for transcript alignment, then text accuracy with spoken audio is improved, but timeline synchronization with video deteriorates due to time lags of 30 seconds or more
Solution Approach 1:
The patent segments the transcript alignment process into two independent components: (1) human-generated closed captions provide accurate word-level text transcription, and (2) automated speech recognition provides accurate time-stamped audio alignment. By separating these functions, the system achieves both text accuracy and timeline synchronization without the 30-second lag problem that occurs when relying solely on human-generated captions.
Solution Approach 2:
The patent introduces automated speech recognition technology as an intermediary component that bridges the gap between human-generated closed captions and video timeline synchronization. The ASR system processes the audio track independently and provides time-stamped transcripts that serve as a reference framework, allowing the human-generated captions to be accurately positioned in time without introducing manual synchronization errors.
2Loss of time
If automated speech recognition software is used alone for transcript generation, then timeline synchronization with video is improved, but text accuracy deteriorates due to transcription errors and missed text especially with background noise
Solution Approach 1:
The patent merges the strengths of two different systems: human-generated closed captions (which provide high text accuracy) and automated speech recognition (which provides accurate timeline synchronization). The system combines these two sources to create a unified transcript that has both accurate transcription and precise time alignment, eliminating the weaknesses of using either system alone.
Solution Approach 2:
The patent creates a composite transcript system that integrates two different data sources with complementary properties. Just as composite materials combine different substances to achieve properties neither material has alone, this system combines human-generated text accuracy with ASR-generated time synchronization to create a transcript that possesses both qualities simultaneously.
3Adaptability or versatility
If traditional closed captioning methods are used, then text display for deaf or hard of hearing individuals is provided, but metadata indexing accuracy deteriorates due to time lag errors in audio transcript alignment
Solution Approach 1:
The patent makes the closed captioning system multi-functional by simultaneously serving accessibility needs and metadata indexing requirements. The same transcript system that provides accurate text display for deaf or hard of hearing individuals also generates precisely aligned metadata for video indexing and searching, eliminating the need for separate systems and ensuring consistent accuracy across both functions.
Data Source
AI summary
The addition of temporal positions to an inverted index allows for temporal queries in addition to phrase queries. Store additional binary data for each term instance in the word-level index to prepare for searching in response to time-based queries from a user is accomplished through the use of Lucene's binary payload feature where the payload structure is defined for use in such searches. The pre-defined payload fields consist of three integers, which account for 12 extra bytes that must be stored for each term instance. A content database on the Master/Administrator server node provides the indexes for search into content in response to user events, returning results in JSON format. The search results may then be used to locate and present content segments to a user containing both requested search term results and the time location and duration within a content asset where the search term(s) is found.