Audio Transcript Segmentation With LLM-Based Segment Naming
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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 manually navigate to locate segments, due to the lack of semantic segmentation and accurate naming.
Innovation Solution
The method involves dividing digital audio content into non-overlapping chunks, augmenting them with contextual information, and using a natural language model to infer segment boundaries and titles, allowing for 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 with basic metadata only, then the system complexity remains low, but search engines cannot find specific segments and computing resources are wasted
Solution Approach 1:
The patent divides digital audio content into multiple semantically meaningful segments with descriptive titles and timestamps. Instead of treating the entire audio file as a single entity, the system creates a hierarchical structure where the audio content is partitioned into segments that can be independently indexed and searched. This segmentation enables search engines to locate specific segments efficiently without scanning the entire content, resolving the contradiction between search efficiency and system complexity.
2Loss of time
If users manually navigate through digital audio content to locate segments, then no additional processing is required, but computing resources are wasted and user time is lost
Solution Approach 1:
The system performs preliminary segmentation and indexing of digital audio content during the content processing stage, before users need to search for segments. By pre-creating segment boundaries, titles, and metadata, the system eliminates the need for users to manually navigate through content or for playout devices to perform trial-and-error seeking. This preliminary action reduces both user time loss and computing resource waste during playback operations.
3Productivity
If segmentation technologies process entire lengthy digital audio content at once, then segmentation can be performed in a single pass, but the technology is limited to a few thousand tokens and cannot handle lengthy content
Solution Approach 1:
The patent applies segmentation to both the content and the processing methodology. By dividing lengthy digital audio content into smaller chunks or segments that fit within the token limits of segmentation technologies, the system enables processing of arbitrarily long content. Each chunk is processed independently to identify segments and generate titles, then results are combined to form the complete segmentation of the original lengthy content, overcoming the token limit constraint.
Solution Approach 2:
The system transforms the segmentation problem from a single-dimension approach (processing entire content at once) to a multi-dimensional approach by introducing temporal chunking. Content is divided into overlapping or non-overlapping chunks along the time axis, allowing segmentation to occur in multiple passes. This dimensional change enables handling of lengthy content beyond token limits while maintaining segmentation quality through context-aware processing of each chunk.
4Measurement precision
If segmentation and naming are performed in separate stages, then each task can be optimized independently, but information cannot be shared between tasks resulting in less accurate segment titles
Solution Approach 1:
The patent merges the segmentation and naming tasks into a single integrated processing stage. Instead of performing segmentation first and then naming segments in separate passes, the system simultaneously identifies segment boundaries and generates descriptive titles for each segment in one operation. This merging allows information to flow between the two tasks, improving title accuracy by considering segmentation context during naming and vice versa, while avoiding the need for complex multi-stage processing pipelines.
Data Source
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.