Audio Interaction Sectioning for Call Center Insight
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Solution Overview
Problem
Current speech recognition and quality monitoring systems in call centers lack the ability to provide deep insights into interaction effectiveness, as they fail to analyze the flow and dynamics of conversations beyond basic transcription and data analysis, making it difficult to identify factors contributing to effective or inefficient agent interactions.
Innovation Solution
A method and apparatus for automatically sectioning audio interactions into context units based on runtime features, using speech recognition, natural language processing, and machine learning models to classify and group sections, enabling detailed analysis of interaction flow and identifying key segments such as introduction, issue handling, and sales processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automated speech recognition and emotion analysis tools are used, then basic interaction data can be obtained, but deep insights into interaction effectiveness and flow dynamics remain unavailable
Solution Approach 1:
The interaction audio signal is segmented into multiple context units based on runtime features such as speaker changes, silence duration, and speech patterns. Each context unit represents a distinct interaction phase (e.g., greeting, issue presentation, resolution). This segmentation enables detailed analysis of interaction flow dynamics while maintaining manageable data structures for processing.
Solution Approach 2:
The patent introduces a temporal dimension by analyzing the sequence and duration of context units within interactions. By examining the timing, order, and length of different interaction phases, the system gains deep insights into interaction effectiveness and agent performance without requiring complex manual analysis of every conversation detail.
2Measurement precision
If manual quality monitoring is performed, then detailed interaction analysis is possible, but time consumption and productivity are reduced
Solution Approach 1:
The system performs self-service by automatically segmenting interactions into context units and identifying key interaction phases without human intervention. The automated analysis extracts meaningful patterns from audio signals, enabling high-volume monitoring while maintaining detailed analysis precision. This eliminates the need for manual listening while preserving insightful evaluation capabilities.
3Loss of information
If full and accurate transcription is available, then complete interaction data is captured, but understanding of interaction flow and effectiveness remains limited
Solution Approach 1:
The system performs preliminary action by pre-segmenting interactions into context units based on acoustic and speech pattern features before detailed analysis. This preliminary structuring identifies potential interaction phases and boundaries, making subsequent flow analysis significantly easier. The pre-processing step transforms raw transcription data into a structured format that reveals interaction dynamics.
Data Source
AI summary
A method and apparatus for automatically sectioning an interaction into sections, in order to get more insight into interactions. The method and apparatus include training, in which a model is generated upon training interactions and available tagging information, and run-time in which the model is used towards sectioning further interactions. The method and apparatus operate on context units within the interaction, wherein each context unit is characterized by a feature vector relate to textual, acoustic or other characteristics of the context unit.