# Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning

arXiv: https://arxiv.org/abs/2501.15613 · HTML: https://mlsanity.com/paper/2501.15613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2025.

## Abstract (arXiv)

Voice conversion (VC) modifies voice characteristics while preserving linguistic content. This paper presents the Stepback network, a novel model for converting speaker identity using non-parallel data. Unlike traditional VC methods that rely on parallel data, our approach leverages deep learning techniques to enhance disentanglement completion and linguistic content preservation. The Stepback network incorporates a dual flow of different domain data inputs and uses constraints with self-destructive amendments to optimize the content encoder. Extensive experiments show that our model significantly improves VC performance, reducing training costs while achieving high-quality voice conversion. The Stepback network's design offers a promising solution for advanced voice conversion tasks.