Seeing Through the Conversation: Audio-Visual Speech Separation based on
  Diffusion Model
Suyeon Lee Chaeyoung Jung Youngjoon Jang Jaehun Kim Joon Son Chung
The objective of this work is to extract target speaker's voice from a
mixture of voices using visual cues. Existing works on audio-visual speech
separation have demonstrated their performance with promising intelligibility,
but maintaining naturalness remains a challenge. To address this issue, we
propose AVDiffuSS, an audio-visual speech separation model based on a diffusion
mechanism known for its capability in generating natural samples. For an
effective fusion of the two modalities for diffusion, we also propose a
cross-attention-based feature fusion mechanism. This mechanism is specifically
tailored for the speech domain to integrate the phonetic information from
audio-visual correspondence in speech generation. In this way, the fusion
process maintains the high temporal resolution of the features, without
excessive computational requirements. We demonstrate that the proposed
framework achieves state-of-the-art results on two benchmarks, including
VoxCeleb2 and LRS3, producing speech with notably better naturalness.