6 citations · 11 across the 13 of their papers we have counts for
5 papers · 1 filter
Content Leakage in LibriSpeech and Its Impact on the Privacy Evaluation of Speaker Anonymization
Carlos Franzreb, Arnab Das, Tim Polzehl +1
Speaker anonymization aims to conceal a speaker's identity, without considering the linguistic content. In this study, we reveal a weakness of Librispeech, the dataset that is comm…
Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning
Carlos Franzreb, Arnab Das, Tim Polzehl +1
The current privacy evaluation for speaker anonymization often overestimates privacy when a same-gender target selection algorithm (TSA) is used, although this TSA leaks the speake…
Private kNN-VC: Interpretable Anonymization of Converted Speech
Carlos Franzreb, Arnab Das, Tim Polzehl +1
Speaker anonymization seeks to conceal a speaker's identity while preserving the utility of their speech. The achieved privacy is commonly evaluated with a speaker recognition mode…
StarGAN-VC++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings
Arnab Das, Suhita Ghosh, Tim Polzehl +1
Voice conversion (VC) transforms an utterance to sound like another person without changing the linguistic content. A recently proposed generative adversarial network-based VC meth…
Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion
Suhita Ghosh, Arnab Das, Yamini Sinha +3
Speech anonymisation prevents misuse of spoken data by removing any personal identifier while preserving at least linguistic content. However, emotion preservation is crucial for n…