Encoder With Dual Decoder Models For Transcript Analysis
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Solution Overview
Problem
Current audio call transcription techniques fail to effectively separate the situation description part from the trouble description part of a call due to noise and inherent characteristics of human communication, such as filler words and emotional language, making it challenging for computers to accurately summarize conversational transcripts.
Innovation Solution
A machine learning approach using an encoder and dual head decoder machine learning models to extract a situation identifier and trouble identifier from transcripts, where the encoder model encodes a vector representation of the transcript, and the dual head decoder models decode this representation to output specific identifiers, improving the accuracy of computer-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech to text recognition is used to transform audio to transcript, then the transcript can be generated, but the accuracy is reduced due to noise and human language characteristics
Solution Approach 1:
The patent segments the transcript processing into multiple specialized decoder models: a situation decoder that identifies situation identifiers and a trouble decoder that identifies trouble identifiers. This segmentation allows each decoder to focus on specific aspects of the transcript, improving accuracy despite noise by dividing the complex recognition task into manageable, specialized components.
Solution Approach 2:
The patent introduces an intermediary processing layer between the raw transcript and the final analysis. The encoder-decoder architecture with specialized decoders acts as an intermediary that filters and structures the noisy transcript data, transforming it into organized situation and trouble identifiers that are easier to analyze with higher accuracy.
2Extent of automation
If computer-based analysis is applied to conversational transcripts, then analysis capability is provided, but the ability to accurately separate situation and trouble parts is insufficient
Solution Approach 1:
The patent divides the computer analysis function into separate specialized decoders: one for situation identification and one for trouble identification. This segmentation enables the automated system to accurately distinguish and separately process different aspects of the conversation, overcoming the limitation of traditional single-model approaches that cannot effectively separate these elements.
Solution Approach 2:
The patent applies local quality by giving each decoder model specialized functionality tailored to its specific task. The situation decoder is optimized for identifying situation contexts while the trouble decoder is optimized for identifying problem areas. This localized specialization improves overall analysis accuracy by matching computational resources to specific analytical needs.
3Adaptability or versatility
If human language characteristics are preserved in transcripts, then natural communication is maintained, but computer analysis becomes more challenging
Solution Approach 1:
The patent extracts specific structured information (situation identifiers and trouble identifiers) from the unstructured, noisy human language transcript. By taking out only the essential analytical elements needed for computer analysis while preserving the original transcript integrity, the system maintains adaptability to human communication patterns while reducing analysis complexity through focused information extraction.
Data Source
AI summary
Operating an encoder with double decoder machine learning models include executing, on a transcript, an encoder machine learning model to generate an encoder output, and executing a situation decoder machine learning model on the encoder output to obtain a situation model output having a situation identifier, and executing a trouble decoder machine learning model using the encoder output to obtain a trouble identifier. The method further includes outputting the situation identifier and the trouble identifier.