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Mexca Speaker Diarization Experiment

A repository for comparing potential speaker diarization tools to be used in the MEXCA pipeline.

Structure

The repository contains subdirectories for different parts of the experiment:

  • automatic-speech-recognition/: Contains files for exploring automatic speech recognition in Dutch on the DED21 dataset
  • speaker-diarization/: Contains all files for the speaker diarization part
    • embeddings/: Contains the encoded speaker embeddings as .pt files
    • results/: Contains the .rttm files with speaker annotations
    • clustering.py: Script for clustering the speaker embeddings and assigning the speaker labels to speaker segments
    • compare_sd.ipynb: Notebook for comparing the speaker diarization approaches
    • default_parser.py: Helper functions for argument parsing
    • plot_pipelines_results.R: Script for visualizing the pipeline comparison
    • pyannote_sd_compare.ipynb: Notebook for analyzing the results of the pyannote.audio pipeline
    • sd_*.py: Scripts for applying the respective speaker encoding models
    • sd_pipeline_ded21_performance.ipynb: Notebook for analyzing the results of the most promising pipelines on the DED21 dataset
    • speaker_diarization.py: Script to run all speaker encoding scripts after each other
    • speaker_representation.py: Helper functions for performing speaker diarization
  • speaker-segmentation/: Contains all files for the speaker segmentation part
    • results/: Cotains the .rttm files with speech segments
    • seg_pyannote.py: Script for applying speaker segmentation using the pyannote.audio package
  • voice-activity-detection/: Contains all files for the voice activity detection part
    • results/: Contains the .rttm files with speech segments
    • compare_vad.ipynb: Notebook for comparing the voice activity detection approaches
    • custom.conf: Configuration file for the opensmile feature extractor
    • opensmile_helper_functions: Helper functions for extracting opensmile voice activity features
    • vad_*.py: Scripts for applying the voice activity detection models
  • create_ded21_corpus.ipynb: Notebook for creating and exploring the DED21 dataset
  • explore_ami_corpus.ipynb: Notebook for exploring the properties of the AMI corpus
  • rttm.py: Functions for creating, reading, modifying, and writing .rttm files and objects
  • rttm_test.py: Preliminary test suite for rttm.py

Method

We compare multiple pipelines for voice activity detection (VAD), speaker segmentation, speaker diarization, and explore automatic speech recognition. We apply these tools to one public dataset (AMI corpus; single channel; microphone; test set) using the default parameters and minimal postprocessing steps. The second data set (Dutch Election Debate 2021) is not openly available yet (due to copyright issues).

Results

The results of our pipeline comparisons are shown in the notebooks:

In the speaker diarization comparison, the pyannote.audio pipeline outperformed all other candidates, achieving an average diarization error rate of 0.32 on the AMI test set (0.21 without speech overlap) and [0.35, 0.38] on the two parts of the DED21 data set (no speech overlap).

References

Bredin, H. et al. (2020). Pyannote.audio: Neural building blocks for speaker diarization. ICASSP 2020, pp. 7124-7128. URL

Carletta, J. (2006). Announcing the AMI meeting corpus. The ELRA Newsletter 11(1), pp. 3-5. URL

Schumacher, G., Homan, M., & Pipal, C. (March, 2021). Welke dijsttrekker lacht het meest? En hoe? Stuk Rood Vlees. URL