Text Zoning via Meaning Unit Boundary Detection
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Solution Overview
Problem
Automated transcription of audio data often lacks punctuation and speaker separation, making accurate analysis difficult due to the absence of natural context cues.
Innovation Solution
A method for zoning transcription data by identifying and splitting utterances into meaning units using linguistic models to calculate the probability of word boundaries, ensuring appropriate context for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated transcription is performed without punctuation and speaker separation, then the transcription process is simple and fast, but the accuracy of analysis is poor due to lack of context cues
Solution Approach 1:
The patent segments the continuous transcription text into discrete meaning units by identifying boundary words. This segmentation introduces structural organization to the otherwise unstructured transcription data, enabling more accurate analysis while maintaining computational efficiency through rule-based boundary detection rather than complex machine learning models.
Solution Approach 2:
The patent performs preliminary zoning of transcription data into meaning units before analysis. By pre-processing the transcription to identify meaningful segments and their boundaries, the system prepares the data in advance for analysis, improving accuracy without requiring complex real-time processing during the analysis phase.
2Measurement precision
If the transcription is divided into many small meaning units, then the context precision is improved, but the computational complexity increases
Solution Approach 1:
The patent changes the parameter of meaning unit size by using a minimum threshold to filter out overly fragmented units. This parameter adjustment ensures that meaning units are sufficiently large to contain meaningful context while still being small enough to provide precise contextual information, balancing context precision with computational efficiency.
Solution Approach 2:
The system uses the transcription data itself to identify meaning unit boundaries through linguistic patterns and boundary words, rather than requiring external annotation or complex training data. This self-service approach achieves precise contextual segmentation without the computational overhead of supervised learning models.
3Measurement precision
If linguistic models are applied to calculate boundary probabilities, then the zoning accuracy is improved, but the processing time increases
Solution Approach 1:
The patent uses simple, computationally inexpensive linguistic models that can be quickly applied to the transcription data. Rather than employing complex, time-consuming deep learning models, the system uses probabilistic models based on linguistic patterns that provide sufficient accuracy with minimal processing time, effectively using simple, disposable computational approaches.
Solution Approach 2:
The patent replaces complex mechanical processing (heavy computational models) with linguistic rule-based approaches. By substituting complex machine learning systems with simpler linguistic pattern recognition and probability calculations, the system achieves good zoning accuracy without the computational burden of more sophisticated models.
Data Source
AI summary
A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.