Speaker Representation System for Call Center Sentiment Analysis
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Solution Overview
Problem
Existing systems struggle to accurately assess and represent the emotions and sentiments of speakers in telephone conversations, leading to monotonous and repetitive work for call center agents, which negatively impacts their performance and customer satisfaction.
Innovation Solution
A system and method that utilizes an electronic device and a server device, optionally on a cloud network, to analyze audio signals from speakers, determining sentiment metrics and appearance metrics through machine learning models, and generates real-time speaker representations, including graphical and auditory feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio signals are analyzed using machine learning models to determine sentiment metrics, then speaker representation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system comprising audio signal analysis modules and machine learning models that process raw audio signals to extract sentiment metrics. This intermediary layer transforms complex audio data into simplified emotional representations, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based speech analysis systems with machine learning models that automatically learn sentiment patterns from audio data. This substitution enables more accurate speaker representation by leveraging the adaptive capabilities of neural networks rather than predefined rules.
2Ease of operation
If real-time speaker representations are generated, then interaction quality is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing audio signals and maintaining buffered data structures that enable rapid sentiment analysis. The system prepares processing pipelines in advance and uses cached model predictions to reduce real-time computation requirements, thereby improving interaction quality without excessive processing delays.
Solution Approach 2:
The patent employs periodic action by updating speaker representations at optimized intervals rather than continuously processing every audio sample. The system analyzes audio signals at strategic moments during conversations, balancing real-time responsiveness with computational efficiency to maintain high interaction quality while minimizing processing time overhead.
Data Source
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AI summary
System, electronic device, and related methods, in particular a method of operating a system comprising an electronic device is disclosed, the method comprising obtaining one or more audio signals including a first audio signal; determining one or more first sentiment metrics indicative of a first speaker state based on the first audio signal, the one or more first sentiment metrics including a first primary sentiment metric indicative of a primary sentiment state of a first speaker; determining one or more first appearance metrics indicative of an appearance of the first speaker, the one or more first appearance metrics including a first primary appearance metric indicative of a primary appearance of the first speaker; determining a first speaker representation based on the first primary sentiment metric and the first primary appearance metric; and outputting, via the interface of the electronic device, the first speaker representation.