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

Autor:	Meyer, Sarina, Miao, Xiaoxiao, Vu, Ngoc Thang
Rok vydání:	2023
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/OJSP.2023.3344375
Popis:	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. Comment: Accepted by OJSP-ICASSP 2024 https://ieeexplore.ieee.org/document/10365329
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.08049 Zobrazit plný text záznamu View this record from Arxiv