Data Augmentation for Diverse Voice Conversion in Noisy Environments

Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on. However, when source or target speech accents, background noise conditions, or microphone characteristics differ from training, quality voice conversion is not guaranteed. These problems are often left unexamined in VC research, giving rise to frustration in users trying to use pretrained VC models on their own data. We are interested in accent-preserving voice conversion for name pronunciation from self-recorded examples, a domain in which all three of the aforementioned conditions are present, and posit that demonstrating higher performance in this domain correlates with creating VC models that are more usable by otherwise frustrated users. We demonstrate that existing SOTA encoder-decoder VC models can be made robust to these variations and endowed with natural denoising capabilities using more diverse data and simple data augmentation techniques in pretraining.