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Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library.

Volodymyr Khoma1,2, Yuriy Khoma2,3, Vitalii Brydinskyi2,3

  • 1Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study combines speaker diarization and identification to recognize multiple speakers in audio. The group-level approach achieved better identification accuracy than segment-level, though segment-level offers real-time processing.

Keywords:
PyAnnote librarycluster classificationdiarization systemidentification of speakers’ utterancessegmental classification

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Area of Science:

  • Speech processing
  • Biometrics
  • Machine learning

Background:

  • Speaker diarization segments audio and groups utterances by speaker but doesn't identify them.
  • Speaker identification systems typically require single-speaker audio.
  • Identifying multiple interacting speakers in audio is a significant challenge.

Purpose of the Study:

  • To propose and evaluate two novel architectures for multi-speaker identification systems.
  • To combine diarization and identification methods for enhanced speaker recognition.
  • To investigate segment-level and group-level classification approaches for speaker identification.

Main Methods:

  • Developed speaker identification systems using the PyAnnote open-source framework.
  • Employed a combination of diarization and speaker identification techniques.
  • Conducted four experiments to optimize system performance, focusing on distance functions, clustering, segmentation, and embedding window sizes.
  • Utilized the AMI Corpus for performance evaluation.

Main Results:

  • The group-level classification architecture demonstrated superior speaker identification accuracy compared to the segment-level approach.
  • The segment-level approach offers the advantage of real-time processing capabilities.
  • Experimental results guided the selection of optimal distance functions, clustering, segmentation algorithms, and embedding window sizes.

Conclusions:

  • Combined diarization and identification methods effectively address multi-speaker recognition challenges.
  • Group-level classification is more accurate for speaker identification, while segment-level is suitable for real-time applications.
  • The proposed architectures provide a robust framework for multi-speaker identification in complex audio environments.