Summary of Preset-voice Matching For Privacy Regulated Speech-to-speech Translation Systems, by Daniel Platnick et al.
Preset-Voice Matching for Privacy Regulated Speech-to-Speech Translation Systems
by Daniel Platnick, Bishoy Abdelnour, Eamon Earl, Rahul Kumar, Zahra Rezaei, Thomas Tsangaris, Faraj Lagum
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); 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 This paper introduces Preset-Voice Matching (PVM), a novel approach to speech-to-speech translation (S2ST) that addresses concerns around liability and privacy. PVM removes the need for cross-lingual voice cloning by matching the input voice to a similar prior consenting speaker voice in the target language, ensuring compliance with regulations and reducing misuse risks. The proposed framework achieves significant improvements in S2ST system runtime in multi-speaker settings and naturalness of synthesized speech. By leveraging preset voices for dynamic tasks, PVM is the first explicitly regulated S2ST framework that tackles these critical issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem with speech-to-speech translation technology. Right now, this tech can be misused or hurt people’s privacy. The scientists propose a new way to translate voices called Preset-Voice Matching (PVM). PVM makes sure that the translated voice sounds like someone who has given permission for their voice to be used. This approach helps prevent bad things from happening and keeps people safe. The results show that PVM works well in different situations and makes the translation sound more natural. |
Keywords
* Artificial intelligence * Translation
