Video Subtitle Segmentation Using Sentence and Break Detection
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Solution Overview
Problem
Creating video subtitles requires significant labor and time without automated technology, hindering creators from reaching a broader audience and monetizing their content effectively.
Innovation Solution
A method involving machine-trained models to process audio data, identify sentence and intra-sentence breaks, and generate timed subtitle data without punctuation marks, ensuring seamless display of clip texts during specific periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated technology is used for subtitle generation, then productivity and time efficiency are improved, but device complexity increases due to machine-trained models
Solution Approach 1:
The subtitle generation process is divided into distinct stages: audio processing to generate timed script, sentence boundary detection using first machine-trained model, intra-sentence break detection using second machine-trained model, and subtitle data generation. This segmentation allows each component to be optimized independently while maintaining overall system productivity.
Solution Approach 2:
The patent introduces intermediate processing structures including timed scripts with word-level timestamps, sentence boundary markers, and clip text segments. These intermediaries bridge the gap between raw audio data and final subtitle output, enabling complex processing through manageable steps without overwhelming system complexity.
2Manufacturing precision
If machine-trained models are used to identify sentence and intra-sentence breaks, then manufacturing precision of subtitle timing is improved, but device complexity increases
Solution Approach 1:
The timing precision problem is segmented into two independent detection tasks: sentence boundary detection (identifying where sentences end) and intra-sentence break detection (identifying pauses within sentences). Each task uses a specialized machine-trained model optimized for its specific function, improving overall timing accuracy while managing complexity through functional separation.
Solution Approach 2:
The system performs preliminary processing to generate timed scripts with word-level timestamps before applying the machine-trained models. This preliminary action provides structured input data that enhances the precision of subsequent sentence and break detection, reducing the computational complexity required for achieving high timing accuracy.
3Loss of time
If automated subtitle generation is implemented, then loss of time in manual processing is reduced, but reliability may be affected by automated processing errors
Solution Approach 1:
The system incorporates multiple layers of detection and verification: the first machine-trained model detects sentence boundaries with confidence scoring, the second model detects intra-sentence breaks, and the system generates structured clip text segments that can be validated against the original audio. This feedback mechanism maintains reliability by allowing error detection and correction while preserving time efficiency.
Solution Approach 2:
The patent performs preliminary audio processing to generate accurately timestamped timed scripts before applying linguistic analysis. This preliminary action establishes a reliable foundation with precise word-level timing, reducing errors in subsequent processing and maintaining high reliability throughout the automated generation process.
Data Source
AI summary
The present disclosure relates to systems and methods for providing subtitle for a video. The video's audio is transcribed to obtain caption text for the video. A first machine-trained model identifies sentences in the caption text. A second model identifies intra-sentence breaks with in the sentences identified using the first machine-trained model. Based on the identified sentences and intra-sentence breaks, one or more words in the caption text are grouped into a clip caption to be displayed for a corresponding clip of the video.