Audio Stream Segmentation for Real-Time ML Response Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Customer support agents face challenges in promptly identifying appropriate responses due to high call volumes and unpredictable conversations, with existing machine learning techniques being too slow to match the natural pace of verbal interactions.
Innovation Solution
A system that selects and transmits relevant portions of an audio stream to a trained machine learning application using an operating system API, reducing computation time by applying tags based on application events or machine learning analysis to provide real-time response recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire audio stream is transmitted to the machine learning application for analysis, then the analysis completeness is improved, but the computation time increases and real-time processing becomes difficult
Solution Approach 1:
The audio stream is segmented into relevant portions based on application events (e.g., call start, call end, transfer events). Instead of processing the entire audio stream, the system identifies and extracts only the segments containing significant events, thereby reducing computation time while maintaining analysis completeness for critical moments.
Solution Approach 2:
The system extracts only the necessary portions of the audio stream that contain application events relevant to customer support analysis. By taking out and transmitting only these specific segments to the machine learning application, the system achieves real-time processing capability without sacrificing the ability to analyze important conversation elements.
2Measurement precision
If traditional machine learning techniques are used to analyze audio streams, then comprehensive analysis is achieved, but the processing speed is too slow to match natural conversation pace
Solution Approach 1:
The system performs preliminary action by identifying and tagging application events (call start, end, transfers) before transmitting audio segments to the machine learning application. This pre-processing step enables the ML model to focus only on relevant portions of the conversation, significantly improving processing speed while maintaining comprehensive analysis of critical events.
Solution Approach 2:
Instead of continuous processing of the entire audio stream, the system employs periodic action by transmitting discrete audio segments corresponding to specific application events at predetermined moments in the conversation. This approach matches the natural pace of verbal interactions while enabling comprehensive analysis of key events.
3Loss of time
If audio stream data is captured from the application, then real-time analysis capability is improved, but the capture speed is not fast enough for beneficial real-time analysis
Solution Approach 1:
The system introduces an intermediary mechanism that listens to application events (call start, end, transfers) generated by the communication application. This intermediary layer captures event metadata and uses it to identify relevant audio segments, enabling fast capture of critical moments without requiring high-speed capture of the entire audio stream.
Solution Approach 2:
The system performs preliminary action by capturing application event data (timestamps, event types) as audio streams are being generated. This event-based pre-processing enables the system to identify and extract relevant audio portions in real-time, achieving beneficial real-time analysis capability without requiring excessively high capture speeds for the entire stream.
Data Source
AI summary
Described techniques select portions of an audio stream for transmission to a trained machine learning application, which generates response recommendations in real-time. This real-time response is facilitated by the system identifying, selecting and transmitting those portions of the audio stream likely to be most relevant to the conversation. Portions of an audio stream less likely to be relevant to the conversation are identified accordingly and not transmitted. The system may identify the relevant portions of an audio stream by detecting events in a contemporaneous event stream, use a trained machine learning model to identify events in an audio stream, or both.