Dubbing is a post-production process of re-recording actors' dialogues, which isextensively used infilmmaking and video production.

In this work, we propose Glow-TTS, a flow-based generativemodel for parallel TTS that does not require anyexternal aligner.

As humans rely on audio localization cues for full scene awareness,spatial audio is a crucial componentof360 video.

In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network(FRCNN) to solvethe separation task. This model containsbottom-up,top-downandlateral connections tofuse information processed atvarious time-scales represented by stages.

Oncetrained,ourmodelcangeneraterandom samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives.

Our approach is based on a keyobservation about human speech: there isoften ashort pause between each sentence orword.

In this paper, we present VoiceMixer which can effectively decompose and transfer voice style through a novel information bottleneck and adversarial feedback.