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Found 6 papers, 0 papers with code
Voice Anonymization for All -- Bias Evaluation of the Voice Privacy Challenge Baseline System
This study investigates bias in voice anonymization systems within the context of the Voice Privacy Challenge.
Evaluation of the Speech Resynthesis Capabilities of the VoicePrivacy Challenge Baseline B1
Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unnatural sounding voices as a tradeoff.
Deep Learning-based F0 Synthesis for Speaker Anonymization
In a deep learning setting, this is achieved by extracting multiple features from speech, altering the speaker identity, and waveform synthesis.
VoicePrivacy 2022 System Description: Speaker Anonymization with Feature-matched F0 Trajectories
We introduce a novel method to improve the performance of the VoicePrivacy Challenge 2022 baseline B1 variants.
On The Effect Of Coding Artifacts On Acoustic Scene Classification
This paper explores the effect of perceptual audio coding on the classification performance using a DCASE 2020 challenge contribution [1].
Exploring the Importance of F0 Trajectories for Speaker Anonymization using X-vectors and Neural Waveform Models
Voice conversion for speaker anonymization is an emerging field in speech processing research.
