  The goal of voice conversion (VC) is to convert input voice to match the
target speaker's voice while keeping text and prosody intact. VC is usually
used in entertainment and speaking-aid systems, as well as applied for speech
data generation and augmentation. The development of any-to-any VC systems,
which are capable of generating voices unseen during model training, is of
particular interest to both researchers and the industry. Despite recent
progress, any-to-any conversion quality is still inferior to natural speech.
  In this work, we propose a new any-to-any voice conversion pipeline. Our
approach uses automated speech recognition (ASR) features, pitch tracking, and
a state-of-the-art waveform prediction model. According to multiple subjective
and objective evaluations, our method outperforms modern baselines in terms of
voice quality, similarity and consistency.

Sound;Machine Learning;Audio and Speech Processing