arXiv:2211.16065
공식 코드 · Papers with Code
Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline
원문 초록
The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis approach, which is consistent with the zero-evidence concept of privacy. This approach significantly reduces the information in speech related to the speaker's sex while preserving speech content and some consistency in the resulting protected voices.
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Training-Free Voice Conversion with Factorized Optimal Transport
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Private kNN-VC: Interpretable Anonymization of Converted Speech
Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages
kNN-SVC: Robust Zero-Shot Singing Voice Conversion with Additive Synthesis and Concatenation Smoothness Optimization
Playing with Voices: Tabletop Role-Playing Game Recordings as a Diarization Challenge
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