Direct enhancement of pre-trained speech embeddings for speech processing in noisy conditions

Ali, Mohamed Nabih; Brutti, Alessio; Falavigna, Daniele

doi:10.1016/j.csl.2023.101501

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The model using ECAPA-TDNN and cosine distance achieves with 3.11% the lowest EER and can therefore be considered as the best ASV evaluation model. This is consistent with the findings in the ASV field [35], [36]. Accordingly, we choose ECAPA-TDNN + cosine as the primary proposed ASV evaluation model in all following experiments.…”

Section: Methodssupporting

confidence: 81%

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

Meyer,

Miao,

2024

IEEE Open J. Signal Process.

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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.

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Section: Methodssupporting

confidence: 81%