Neural Network Text Summarization With Speaker Segmentation
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Solution Overview
Problem
Speech-to-text transcription services often produce inaccurate text summaries due to complexities in audio transcripts, such as unclear speaker identification.
Innovation Solution
A neural network-based system that preprocesses audio recordings into segments, transcribes and normalizes them, and uses a large language model to generate summaries with context awareness, ensuring accurate speaker identification and summary format adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech-to-text transcription services are used to generate text summaries, then text summaries can be produced automatically, but inaccuracies occur due to contextual nuances and unclear speaker identification
Solution Approach 1:
The patent segments the audio transcript by identifying and separating different speakers' contributions. The system divides the continuous transcript into speaker-specific segments, allowing each speaker's context to be preserved and analyzed independently. This segmentation enables the neural network to process each speaker's statements with proper contextual understanding, thereby improving summary accuracy while maintaining automatic generation.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw transcript and the final summary. This intermediary layer includes speaker identification modules and context analysis components that mediate the transformation of raw speech data into structured, context-aware text representations. The neural network then processes these refined intermediate representations to generate accurate summaries, resolving the accuracy issue while preserving automation.
2Speed
If traditional transcription services process audio transcripts, then text output is generated quickly, but contextual nuances are lost leading to inaccuracies
Solution Approach 1:
The patent performs preliminary actions by pre-processing the audio transcript to identify speakers and establish contextual relationships before the main summarization task. The system analyzes speaker patterns, identifies contextual cues, and structures the data with metadata about speaker identities and relationships. This preliminary processing preserves contextual information in an organized format that can be quickly processed by the neural network, maintaining both speed and information integrity.
Solution Approach 2:
The patent adds another dimension to the transcription process by incorporating speaker identification and contextual metadata as additional layers of information. Instead of producing a single-dimensional text transcript, the system creates multi-dimensional data structures that include speaker attributes, contextual relationships, and semantic annotations. This dimensional enrichment allows the neural network to access contextual information efficiently without sacrificing processing speed.
Data Source
AI summary
Apparatuses, systems, and techniques to cause one or more neural networks to summarize a text. In at least one embodiment, a processor is to cause one or more neural networks to generate one or more summaries of a first portion of a text based, at least in part, on one or more second portions of said text.