'Voice Conversion' paper candidate 2411.16147

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article info.

• title: SKQVC: One-Shot Voice Conversion by K-Means Quantization with Self-Supervised Speech Representations

• summary: One-shot voice conversion (VC) is a method that enables the transformation
  between any two speakers using only a single target speaker utterance. Existing
  methods often rely on complex architectures and pre-trained speaker
  verification (SV) models to improve the fidelity of converted speech. Recent
  works utilizing K-means quantization (KQ) with self-supervised learning (SSL)
  features have proven capable of capturing content information from speech.
  However, they often struggle to preserve speaking variation, such as prosodic
  detail and phonetic variation, particularly with smaller codebooks. In this
  work, we propose a simple yet effective one-shot VC model that utilizes the
  characteristics of SSL features and speech attributes. Our approach addresses
  the issue of losing speaking variation, enabling high-fidelity voice conversion
  trained with only reconstruction losses, without requiring external speaker
  embeddings. We demonstrate the performance of our model across 6 evaluation
  metrics, with results highlighting the benefits of the speaking variation
  compensation method.

• id: http://arxiv.org/abs/2411.16147v1

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