Conversational Voice Data Key Information Extraction
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Solution Overview
Problem
Current call management systems lack an efficient method to automatically identify and extract key information from conversational voice data, which hinders their ability to process and analyze customer interactions effectively.
Innovation Solution
A method and apparatus that transcribes conversational audio into text, removes non-essential stop words, identifies feature words by frequency, and computes correlation scores to determine key questions and responses, reducing the conversation to a lower dimensional space for easier processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated identification of key information is implemented, then productivity and analysis capability are improved, but device complexity increases
Solution Approach 1:
The system segments the conversation processing into distinct stages: transcription of audio to text, identification of speaker turns, extraction of feature words, computation of correlation scores, and identification of key questions and responses. This segmentation allows each component to be optimized independently and reduces overall system complexity while maintaining high processing throughput.
Solution Approach 2:
The system transforms the conversation from a high-dimensional space (raw audio waves) to a lower-dimensional space (extracted feature words and correlation scores). By converting audio signals into textual representations and then into structured data with correlation metrics, the system reduces dimensionality and makes the data more manageable for analysis while preserving key information.
2Loss of time
If key information extraction is automated, then loss of time is reduced, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary actions by first transcribing the audio to text and pre-identifying feature words before computing correlation scores. This preliminary processing enables the system to quickly identify key information without requiring complex real-time analysis, thus reducing processing time while maintaining accuracy through structured extraction methods.
Solution Approach 2:
The system replaces manual analysis of conversations with automated computational methods. By using algorithms to transcribe audio, identify feature words, and compute correlation scores, the system eliminates time-consuming manual processing while maintaining precision through structured computational approaches that can consistently apply the same criteria for information extraction.
3Loss of information
If conversational data is processed in original form, then information completeness is maintained, but processing load increases
Solution Approach 1:
The system extracts only the essential information from the full conversational data by identifying and isolating feature words that carry meaningful content. By separating these feature words from non-essential elements like filler words and stop words, the system maintains information completeness regarding key topics while significantly reducing the processing load required to analyze the data.
Solution Approach 2:
The system reduces the dimensionality of conversational data by transforming it from raw audio to extracted feature word representations. This dimensional reduction preserves the essential information structure while making the data much more efficient to process, as the extracted features capture the meaningful content without the overhead of complete conversational context.
Data Source
AI summary
A computer-implemented method and apparatus for extracting key information from conversational voice data, where the method comprises receiving a first speaker text corresponding to a speech of a first speaker in a conversation with a second speaker, the conversation comprising multiple turns of speech between the first speaker and the second speaker, the first speaker text comprising multiple question lines, each question line corresponding to the speech of the first speaker at a corresponding turn, arranged chronologically. Feature words are identified, and a frequency of occurrence therefor in each question line is determined. Question lines without any of the feature words are removed, to yield candidate question lines, for each of which a mathematical representation is generated. A similarity score for each candidate question line with respect to each subsequent candidate question line is computed, and the line with the highest score is identified as a key question.