'Voice Conversion' paper candidate 2411.19770

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• title: Noro: A Noise-Robust One-shot Voice Conversion System with Hidden Speaker Representation Capabilities

• summary: One-shot voice conversion (VC) aims to alter the timbre of speech from a
  source speaker to match that of a target speaker using just a single reference
  speech from the target, while preserving the semantic content of the original
  source speech. Despite advancements in one-shot VC, its effectiveness decreases
  in real-world scenarios where reference speeches, often sourced from the
  internet, contain various disturbances like background noise. To address this
  issue, we introduce Noro, a Noise Robust One-shot VC system. Noro features
  innovative components tailored for VC using noisy reference speeches, including
  a dual-branch reference encoding module and a noise-agnostic contrastive
  speaker loss. Experimental results demonstrate that Noro outperforms our
  baseline system in both clean and noisy scenarios, highlighting its efficacy
  for real-world applications. Additionally, we investigate the hidden speaker
  representation capabilities of our baseline system by repurposing its reference
  encoder as a speaker encoder. The results shows that it is competitive with
  several advanced self-supervised learning models for speaker representation
  under the SUPERB settings, highlighting the potential for advancing speaker
  representation learning through one-shot VC task.

• id: http://arxiv.org/abs/2411.19770v1

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