Video Information Extraction via Speech-to-Text Summaries and Trends
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Solution Overview
Problem
The increasing volume of user-generated video content and diverse topics make manual analysis challenging, necessitating efficient methods to extract and analyze information from video data.
Innovation Solution
A method and device for extracting and analyzing video data by detecting speech, generating text and summary data, and identifying trends using keyword and tag data, with interfaces for displaying and interacting with the analyzed information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of video data is performed, then analysis accuracy can be maintained, but productivity decreases due to the increasing volume of user-generated content
Solution Approach 1:
The video analysis system is divided into multiple independent modules: speech detection module, text generation module, summary generation module, and trend analysis module. Each module processes specific aspects of video data independently, enabling parallel processing and improving overall productivity while maintaining manageable complexity through modular architecture.
Solution Approach 2:
Text data serves as an intermediary between video content and analysis results. The system first converts video speech to text, then performs text-based processing for summary and trend generation. This intermediary approach enables efficient automated processing while preserving the semantic information needed for accurate analysis.
2Measurement precision
If comprehensive speech detection and text generation is performed on all video data, then information extraction accuracy improves, but loss of time increases due to processing volume
Solution Approach 1:
The system extracts only the speech audio tracks from video files and converts them to text using speech-to-text technology. This extraction approach focuses processing resources on the most informative element (speech) while ignoring redundant visual data, improving both accuracy and efficiency.
Solution Approach 2:
Speech-to-text conversion is performed as a preliminary step before summary generation and trend analysis. By converting speech to text upfront, the system enables subsequent text-based processing to proceed faster, reducing overall processing time while maintaining detection accuracy.
3Loss of information
If detailed summary data and trend data are generated for all videos, then information completeness improves, but device complexity increases due to multiple processing steps
Solution Approach 1:
The text data generated from speech detection serves multiple purposes: it is used for generating video summaries, analyzing trends, and identifying key topics. This multi-functional use of the same text data eliminates redundant processing steps and reduces system complexity while maintaining information completeness.
Solution Approach 2:
The system combines summary generation and trend analysis into a unified processing pipeline that operates on the same text data. By merging these functions and sharing common processing resources, the system reduces overall complexity while ensuring comprehensive information extraction through coordinated analysis.
Data Source
AI summary
A device and method for extracting and analyzing information from video data includes obtaining video data; detecting speech uttered within the video data to generate text data for the video data, the text data comprising a speech text of the video data, generating summary data for the video data based on the text data, generating trend data for the video data based on tag data; and displaying information about the video data.