Audio Transcript Segmentation for Direct Segment Search
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Solution Overview
Problem
Current digital audio content indexing methods treat audio files as monolithic entities, limiting search engines' ability to find specific segments and wasting computing resources when users navigate through content to locate desired segments.
Innovation Solution
The method involves segmenting digital audio content into semantically coherent chunks, augmenting them with contextual information, and using a natural language model to determine accurate segment titles, allowing efficient indexing and direct navigation to specific segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital audio content is treated as a monolithic entity for indexing, then the indexing process is simple, but search engines cannot find specific segments and computing resources are wasted
Solution Approach 1:
The patent divides digital audio content into multiple segments based on semantic boundaries detected through transcript analysis. Each segment represents a distinct topic or subject matter, allowing search engines to index and retrieve specific portions rather than treating the entire audio file as a single unit. This segmentation enables efficient content discovery while maintaining manageable indexing complexity through automated processing.
2Loss of time
If users navigate through digital audio content to locate specific segments, then no additional indexing is needed, but computing resources are wasted and user time is lost
Solution Approach 1:
The patent performs preliminary segmentation and indexing of digital audio content into semantically meaningful segments before users need to access it. By pre-processing the content to identify segment boundaries and create an indexed structure, the system enables users to directly navigate to specific segments without trial-and-error searching, thereby reducing both time loss and computing resource usage during playback.
3Adaptability or versatility
If segmentation is performed subjectively with different boundaries, then flexibility is increased, but consistency and accuracy of segment titles decrease
Solution Approach 1:
The patent employs an iterative feedback mechanism where segment titles are generated based on transcript analysis, then refined through multiple processing passes. The system analyzes transcript text to identify semantic boundaries, generates initial segment titles, and refines them through feedback loops that improve consistency and accuracy while maintaining the flexibility to adapt to different content types and segmentation preferences.
Data Source
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AI summary
Example implementations include dividing a textual transcript of digital audio content into a sequence of chunks, where the chunks are chronologically non-overlapping; determining annotations for each of the chunks, the annotations including at least one of: a title of the digital audio content, a description of the digital audio content, or one or more inferred segment titles of one or more previous segments of the digital audio content; providing, to a natural language model, a first chunk from the sequence of chunks, an associated annotation, and instructions to identify: a segment found in the first chunk, and a segment title of the segment; receiving, from the natural language model, an indication of the segment and the segment title; and storing the indication of the segment and the segment title as metadata associated with the digital audio content.