  Audio-visual speech enhancement aims to extract clean speech from a noisy
environment by leveraging not only the audio itself but also the target
speaker's lip movements. This approach has been shown to yield improvements
over audio-only speech enhancement, particularly for the removal of interfering
speech. Despite recent advances in speech synthesis, most audio-visual
approaches continue to use spectral mapping/masking to reproduce the clean
audio, often resulting in visual backbones added to existing speech enhancement
architectures. In this work, we propose LA-VocE, a new two-stage approach that
predicts mel-spectrograms from noisy audio-visual speech via a
transformer-based architecture, and then converts them into waveform audio
using a neural vocoder (HiFi-GAN). We train and evaluate our framework on
thousands of speakers and 11+ different languages, and study our model's
ability to adapt to different levels of background noise and speech
interference. Our experiments show that LA-VocE outperforms existing methods
according to multiple metrics, particularly under very noisy scenarios.
