A SYSTEM FOR COMPREHENSIVE ANALYSIS OF COMMUNICATIVE

Authors

  • Rudensky Roman National University of Life and Environmental Sciences of Ukraine image/svg+xml
  • Kravchenko Volodymyr National University of Life and Environmental Sciences of Ukraine image/svg+xml

Keywords:

Speaker Diarization, Automatic Speech Recognition, Artificial Intelligence, Public Discussions, Analysis of Communicative Behavior

Abstract

The relevance of this study is driven by the increasing volume of online meetings and public discussions in digital formats, creating a demand for automated tools to analyze group communication. Traditional manual coding and transcription methods are highly labor-intensive and subjective, limiting large-scale research on communication patterns. The aim of this research is to develop and validate a comprehensive system for automated analysis of communicative behavior that integrates modern speaker diarization technologies, automatic speech recognition, and statistical analysis to provide a detailed picture of group dynamics in public discussions. Methods. The system is implemented using a microservice architecture with Python 3.10+, FastAPI, and React. Speaker diarization is performed using the pyannote.audio algorithm, which combines convolutional encoders with pre-trained WavLM models. Automatic speech recognition is carried out using transformer architectures (Whisper, AssemblyAI, Conformer). Communicative behavior analysis includes calculation of activity statistics, network analysis of interactions, and assessment of communication style. Results. The developed system successfully integrates speaker diarization with 0.5-second precision, automatic transcription, and multidimensional analysis of communication patterns. The modular architecture ensures flexibility for adaptation to various application domains. The system generates detailed timestamps of participant activity, visualizes speaking time distribution, and provides comprehensive analytics to improve decision-making processes. Prospects. Further development of the system includes integration of multimodal analysis considering non-verbal communication, improvement of stability in noisy conditions, domain adaptation for specific sectors, and implementation of real-time analysis of live discussions. The system opens new opportunities for studying group dynamics in corporate, educational, and governmental sectors.

References

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Published

2026-02-02

Issue

No. 2 (2025)

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All articles from the issue

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