VoicePAT: An Efficient Open-Source Evaluation Toolkit for Voice Privacy Research

Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the compari...

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Bibliographic Details
Published inIEEE open journal of signal processing Vol. 5; pp. 257 - 265
Main Authors Meyer, Sarina, Miao, Xiaoxiao, Vu, Ngoc Thang
Format Journal Article
LanguageEnglish
Published New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Data privacy
Information filtering
Information integrity
Measurement
Modular structures
Open source software
Pipelines
Privacy
privacy evaluation
Source code
Speaker anonymization
Synthetic aperture sonar
voice privacy
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Summary:Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the comparison and combination of different anonymization approaches remains challenging due to the complexity of evaluation and the absence of user-friendly research frameworks. We therefore propose an efficient speaker anonymization and evaluation framework based on a modular and easily extendable structure, almost fully in Python. The framework facilitates the orchestration of several anonymization approaches in parallel and allows for interfacing between different techniques. Furthermore, we propose modifications to common evaluation methods which improves the quality of the evaluation and reduces their computation time by 65 to 95%, depending on the metric. Our code is fully open source.
ISSN:2644-1322
2644-1322
DOI:10.1109/OJSP.2023.3344375