  Recent research has demonstrated impressive results in video-to-speech
synthesis which involves reconstructing speech solely from visual input.
However, previous works have struggled to accurately synthesize speech due to a
lack of sufficient guidance for the model to infer the correct content with the
appropriate sound. To resolve the issue, they have adopted an extra speaker
embedding as a speaking style guidance from a reference auditory information.
Nevertheless, it is not always possible to obtain the audio information from
the corresponding video input, especially during the inference time. In this
paper, we present a novel vision-guided speaker embedding extractor using a
self-supervised pre-trained model and prompt tuning technique. In doing so, the
rich speaker embedding information can be produced solely from input visual
information, and the extra audio information is not necessary during the
inference time. Using the extracted vision-guided speaker embedding
representations, we further develop a diffusion-based video-to-speech synthesis
model, so called DiffV2S, conditioned on those speaker embeddings and the
visual representation extracted from the input video. The proposed DiffV2S not
only maintains phoneme details contained in the input video frames, but also
creates a highly intelligible mel-spectrogram in which the speaker identities
of the multiple speakers are all preserved. Our experimental results show that
DiffV2S achieves the state-of-the-art performance compared to the previous
video-to-speech synthesis technique.
