  Speech enhancement has seen great improvement in recent years mainly through
contributions in denoising, speaker separation, and dereverberation methods
that mostly deal with environmental effects on vocal audio. To enhance speech
beyond the limitations of the original signal, we take a regeneration approach,
in which we recreate the speech from its essence, including the semi-recognized
speech, prosody features, and identity. We propose a wav-to-wav generative
model for speech that can generate 24khz speech in a real-time manner and which
utilizes a compact speech representation, composed of ASR and identity
features, to achieve a higher level of intelligibility. Inspired by voice
conversion methods, we train to augment the speech characteristics while
preserving the identity of the source using an auxiliary identity network.
Perceptual acoustic metrics and subjective tests show that the method obtains
valuable improvements over recent baselines.
