Video Question Answering Using Speech and Image Text Extraction
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Solution Overview
Problem
Existing question answer systems struggle to provide accurate search results for information contained in the speech or image within video content, as they primarily rely on video titles and descriptions.
Innovation Solution
A question answer system that analyzes speech and image content in videos, converting it into text data using deep learning models, and employs a machine reading comprehension engine to extract answers, providing time stamps for precise video section display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing question answer systems use only video titles and descriptions for search, then the system complexity is low, but the search accuracy and information retrieval reliability are insufficient
Solution Approach 1:
The video content is segmented into multiple sections based on screen switching points, and speech recognition is performed separately for each section. This segmentation allows the system to process and analyze video content more effectively, improving search accuracy by examining actual video content rather than relying solely on titles and descriptions.
Solution Approach 2:
A machine reading comprehension engine is introduced as an intermediary component that receives text data from speech recognition, performs natural language processing, and generates accurate search results. This intermediary layer enables the system to understand and analyze video content deeply, resolving the contradiction between improved reliability and increased complexity.
2Loss of information
If the system analyzes only video metadata, then the processing time is short, but the information completeness and answer reliability are insufficient
Solution Approach 1:
The video is pre-divided into sections based on screen switching points before speech recognition is performed. This preliminary segmentation structure enables more efficient processing during search operations, as the system can quickly locate and analyze relevant sections without processing the entire video content, thus reducing processing time while maintaining information completeness.
Solution Approach 2:
The system performs speech recognition and analysis on selected video sections rather than the entire video. By focusing on relevant sections identified through screen switching points and time stamps, the system achieves sufficient information completeness for accurate search results while minimizing processing time through selective analysis.
3Productivity
If speech recognition is performed on the entire video at once, then the information extraction is comprehensive, but the processing efficiency and user experience are reduced
Solution Approach 1:
The video is divided into multiple sections based on screen switching points, and speech recognition is performed separately for each section. This segmentation improves processing efficiency by allowing parallel processing and reducing memory requirements, while still achieving comprehensive information extraction across the entire video content.
Solution Approach 2:
The video sections are pre-divided and organized with time stamps before speech recognition. This preliminary structuring enables efficient retrieval and processing during search operations, maintaining high productivity while ensuring complete information extraction from all video sections when needed.
Data Source
AI summary
A question answer system for automatically generating an answer to a question of a user according to an exemplary embodiment of the present disclosure includes a user interface configured to receive a video URL address and the question from the user, a video analysis unit configured to download a video through the video URL address, divide the video into a plurality of sections, and recognize a speech, convert the speech into text, and extract text included in an image for each section to generate content of the video as text data, and a machine reading comprehension engine configured to receive the text data from the video analysis unit and extract the answer to the question from the text data.