Summary of Improving Voice Quality in Speech Anonymization with Just Perception-informed Losses, by Suhita Ghosh et al.
Improving Voice Quality in Speech Anonymization With Just Perception-Informed Losses
by Suhita Ghosh, Tim Thiele, Frederic Lorbeer, Frank Dreyer, Sebastian Stober
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning approach to speech anonymization is proposed, which aims to obscure a speaker’s identity while retaining critical information for subsequent tasks. The method employs voice conversion techniques and uses loss functions inspired by the human auditory system. A VQVAE-based model, enhanced with perception-driven losses, outperforms the vanilla model in terms of naturalness, intelligibility, and prosody while maintaining speaker anonymity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speech anonymization is important for protecting people’s identities when using cloud-based speech assistants. Researchers have developed a new method that uses voice conversion to hide who’s speaking while keeping important information. The approach uses special loss functions that mimic how humans hear sounds. A model called VQVAE, with these custom losses, does better than other models in making the anonymized voices sound natural and understandable. |
Keywords
» Artificial intelligence » Machine learning
