Perceptual implications of automatic anonymization in pathological speech

Overview

• This is the official repository of the paper Perceptual implications of automatic anonymization in pathological speech.

Abstract

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Prerequisites

The software is developed in Python 3.10. For the deep learning, the PyTorch 2.1 framework is used.

Main Python modules required for the software can be installed from ./requirements:

$ conda env create -f requirements.yaml
$ conda activate perceptual

Note: This might take a few minutes.

Code structure

Our source code for training and evaluation of the deep neural networks, speech analysis and preprocessing are available here.

• You can run all statistical analyses from ./analyses.py.

Structure of the PathologyAnonym project:

1. Everything can be run from ./PathologyAnonym_main.py.

• The data preprocessing parameters, directories, hyper-parameters, and model parameters can be modified from ./PathologyAnonym_main/configs/config.yaml.
• Also, you should first choose an experiment name (if you are starting a new experiment) for training, in which all the evaluation and loss value statistics, tensorboard events, and model & checkpoints will be stored. Furthermore, a config.yaml file will be created for each experiment storing all the information needed.
• For testing, just load the experiment which its model you need.

2. The rest of the files:

• ./PathologyAnonym_main/data/ directory contains all the data preprocessing, and loading files.
• ./PathologyAnonym_main/mcAdams_Anonym/ directory contains all the files for anonymization using McAdams coefficient method.
• ./PathologyAnonym_main/PathologyAnonym_Train_Valid.py contains the training and validation processes.
• ./PathologyAnonym_main/pathanonym_Prediction.py all the prediction and testing processes.
• For EER calculation you should use either of the anonymization methods' folders based on your need.

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In case you use this repository, please cite the original papers:

1:

Tayebi Arasteh S, et al. Perceptual implications of automatic anonymization in pathological speech. ArXiv (2025).

BibTex

@article {pathology_perceptual,
  year = {2025},
}

2:

Tayebi Arasteh S, Arias-Vergara T, Pérez-Toro P, et al. Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech. Communications Medicine, 4, 182 (2024). DOI: https://doi.org/10.1038/s43856-024-00609-5

BibTex

@article {pathology_anonym,
  author={Tayebi Arasteh, Soroosh and Arias-Vergara, Tomás and Pérez-Toro, Paula Andrea and Weise, Tobias and Packhäuser, Kai and Schuster, Maria and Noeth, Elmar and Maier, Andreas and Yang, Seung Hee},
  title = {Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech},
  volume={4},
  ISSN={2730-664X},
  url={http://dx.doi.org/10.1038/s43856-024-00609-5},
  DOI={10.1038/s43856-024-00609-5},
  journal={Communications Medicine},
  year = {2024},
}

3:

Tayebi Arasteh S, Weise T, Schuster M, et al. The effect of speech pathology on automatic speaker verification: a large-scale study. Scientific Reports (2023) 13:20476. https://doi.org/10.1038/s41598-023-47711-7

BibTex

@article {pathology_asv,
author = {Tayebi Arasteh, Soroosh and Weise, Tobias, and Schuster, Maria and Noeth, Elmar and Maier, Andreas and Yang, Seung Hee},
title = {The effect of speech pathology on automatic speaker verification: a large-scale study},
year = {2023},
pages = {20476},
volume = {13},
doi = {https://doi.org/10.1038/s41598-023-47711-7},
journal = {Scientific Reports}
}