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

• URL: http://arxiv.org/abs/2501.15613v1
• Date: Sun, 26 Jan 2025 17:43:32 GMT
• Title: Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning
• Authors: Qian Yang, Calbert Graham,
• Abstract summary: This paper presents a novel model for converting speaker identity using non-parallel data.<n>Deep learning techniques are used to enhance disentanglement completion and linguistic content preservation.<n>The Stepback network's design offers a promising solution for advanced voice conversion tasks.
• Score: 22.866607731480638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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