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eess.AS2023
Voice Anonymization for All -- Bias Evaluation of the Voice Privacy Challenge Baseline System
Anna Leschanowsky, Ünal Ege Gaznepoglu, Nils Peters
In an age of voice-enabled technology, voice anonymization offers a solution to protect people's privacy, provided these systems work equally well across subgroups. This study inve…
eess.AS2023
Evaluation of the Speech Resynthesis Capabilities of the VoicePrivacy Challenge Baseline B1
Ünal Ege Gaznepoglu, Nils Peters
Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unna…
eess.AS2023★ 1 cited
Deep Learning-based F0 Synthesis for Speaker Anonymization
Ünal Ege Gaznepoglu, Nils Peters
Voice conversion for speaker anonymization is an emerging concept for privacy protection. In a deep learning setting, this is achieved by extracting multiple features from speech,…