In recent years, there has been increased demand for speech-to-speech translation (S2ST) systems in industry settings. Although successfully commercialized, cloning-based S2ST systems expose their distributors to liabilities when misused by individuals and can infringe on personality rights when exploited by media organizations. This work proposes a regulated S2ST framework called Preset-Voice Matching (PVM). PVM removes cross-lingual voice cloning in S2ST by first matching the input voice to a similar prior consenting speaker voice in the target-language. With this separation, PVM avoids cloning the input speaker, ensuring PVM systems comply with regulations and reduce risk of misuse. Our results demonstrate PVM can significantly improve S2ST system run-time in multi-speaker settings and the naturalness of S2ST synthesized speech. To our knowledge, PVM is the first explicitly regulated S2ST framework leveraging similarly-matched preset-voices for dynamic S2ST tasks.
@article{arxiv.2407.13153,
title = {Preset-Voice Matching for Privacy Regulated Speech-to-Speech Translation Systems},
author = {Daniel Platnick and Bishoy Abdelnour and Eamon Earl and Rahul Kumar and Zahra Rezaei and Thomas Tsangaris and Faraj Lagum},
journal= {arXiv preprint arXiv:2407.13153},
year = {2024}
}
Comments
Accepted to the ACL PrivateNLP 2024 Workshop, 7 pages, 2 figures