  Fine-grained editing of speech attributes$\unicode{x2014}$such as prosody
(i.e., the pitch, loudness, and phoneme durations), pronunciation, speaker
identity, and formants$\unicode{x2014}$is useful for fine-tuning and fixing
imperfections in human and AI-generated speech recordings for creation of
podcasts, film dialogue, and video game dialogue. Existing speech synthesis
systems use representations that entangle two or more of these attributes,
prohibiting their use in fine-grained, disentangled editing. In this paper, we
demonstrate the first disentangled and interpretable representation of speech
with comparable subjective and objective vocoding reconstruction accuracy to
Mel spectrograms. Our interpretable representation, combined with our proposed
data augmentation method, enables training an existing neural vocoder to
perform fast, accurate, and high-quality editing of pitch, duration, volume,
timbral correlates of volume, pronunciation, speaker identity, and spectral
balance.
