Parallel voice conversion with limited training data using stochastic variational deep kernel learning

There are two types of voice conversion methods: statistical and deep learning-based. Although statistical methods can train with limited data, they face challenges, including spectral oversmoothing and time-domain discontinuity. On the other hand, extensively researched deep learning-based methods rely primarily on massive amounts of data, which limits their practical applicability. Given that voice conversion is an engineering problem with limited training data, it is crucial to develop techniques that can produce satisfactory results in terms of quality and similarity in the absence of a large amount of data.