Multi-Speaker Voice Signal Analysis for Call Quality Metrics
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Solution Overview
Problem
Existing technologies lack comprehensive methods to analyze and evaluate the quality of telephone calls, including conversational dynamics, acoustic fidelity, and paralinguistic aspects, particularly in multi-channel and multi-speaker interactions, without being language-dependent.
Innovation Solution
A system and method for voice signal analysis on telephone calls that employs statistical methodologies and machine learning models to compute dialogue-related, acoustic quality, and paralinguistic metrics, using preprocessing, metrics computation, tagging, and alarming modules to assess and synthesize performance indicators, optionally generating reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive quality attributes including conversational dynamics, acoustic fidelity, and paralinguistic aspects are analyzed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the voice signal analysis into three distinct computational dimensions: conversational dynamic analysis, acoustical fidelity assessment, and paralinguistic cues interpretation. Each dimension processes specific metrics independently, allowing comprehensive quality evaluation while managing complexity through modular organization of analysis tasks.
Solution Approach 2:
The system transitions from traditional single-dimension voice quality assessment to a multi-dimensional evaluation framework that simultaneously analyzes conversational dynamics, acoustic properties, and paralinguistic features. This dimensional expansion enables comprehensive quality measurement without requiring a single overly complex analysis module.
2Adaptability or versatility
If language-independent analysis is implemented, then adaptability is improved, but measurement precision may worsen
Solution Approach 1:
The system extracts and analyzes only those voice signal features that are universally present across all languages, such as acoustic properties, paralinguistic cues, and conversational dynamics. By separating language-specific content from language-independent structural and acoustic characteristics, the system achieves both language independence and maintained measurement precision.
Solution Approach 2:
The system changes the analyzed parameters from language-specific textual content to language-independent acoustic and behavioral parameters. This parameter transformation allows the system to evaluate voice quality across different languages while maintaining consistent and precise measurement criteria based on universal voice signal characteristics.
3Measurement precision
If multiple metrics including dialogue-related, acoustic quality, and paralinguistic metrics are computed, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system merges the three distinct metric computation processes (conversational dynamics, acoustic quality, and paralinguistic cues) into a unified analysis framework that processes voice signals simultaneously across all dimensions. This consolidation reduces redundant data processing and information loss while maintaining comprehensive measurement precision through integrated metric computation.
Data Source
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AI summary
A method for analyzing voice signals on telephone calls to assess the quality of telecommunication services comprehensively is disclosed. The method comprises the preprocessing stage (100), the metrics computation stage (200), the tagging stage (300). The metrics computation stage (200) is based on dialogue-related aspects of the conversation among speakers, the acoustic quality of the voice signal, and paralinguistic aspects of the speech segments within the voice signal. Optionally, the method comprises establishing alarms (400) and generating comprehensive reports (500). It is a multiple channel and speaker telephone conversation-oriented method that is language independent. This voice signal evaluation allows the online monitoring, including speech and non-speech segments during calls, as well as the offline retrospective evaluation of the telecommunication service segmented by criteria such as geographical regions or call center.