The effect of speech pathology on automatic speaker verification

Overview

• This is the official repository of the paper The effect of speech pathology on automatic speaker verification: a large-scale study.

Abstract

Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech.

Prerequisites

The software is developed in Python 3.9. For the deep learning, the PyTorch 1.13 framework is used.

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

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

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.

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

• The data preprocessing parameters, directories, hyper-parameters, and model parameters can be modified from ./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:

• ./data/ directory contains all the data preprocessing, and loading files.
• ./speaker_Train_Valid.py contains the training and validation processes.
• ./speaker_Prediction.py all the prediction and testing processes.

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

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}
}