RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations
This work addresses the challenge of generating accurate speech from silent videos for applications like assistive technologies, though it is incremental as it builds on existing non-autoregressive architectures.
The paper tackles the problem of speaker-dependent lip-to-speech synthesis by proposing RobustL2S, a modular framework that disentangles speech content from ambient information and speaker characteristics, achieving state-of-the-art performance on datasets like Lip2Wav, GRID, and TCD-TIMIT.
Significant progress has been made in speaker dependent Lip-to-Speech synthesis, which aims to generate speech from silent videos of talking faces. Current state-of-the-art approaches primarily employ non-autoregressive sequence-to-sequence architectures to directly predict mel-spectrograms or audio waveforms from lip representations. We hypothesize that the direct mel-prediction hampers training/model efficiency due to the entanglement of speech content with ambient information and speaker characteristics. To this end, we propose RobustL2S, a modularized framework for Lip-to-Speech synthesis. First, a non-autoregressive sequence-to-sequence model maps self-supervised visual features to a representation of disentangled speech content. A vocoder then converts the speech features into raw waveforms. Extensive evaluations confirm the effectiveness of our setup, achieving state-of-the-art performance on the unconstrained Lip2Wav dataset and the constrained GRID and TCD-TIMIT datasets. Speech samples from RobustL2S can be found at https://neha-sherin.github.io/RobustL2S/