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LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading
Summary
This study introduces LipSound2, a novel method for reconstructing speech from video using self-supervised learning. LipSound2 significantly enhances speech quality and intelligibility, achieving state-of-the-art results in lip reading tasks.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Speech Processing
Background:
- Crossmodal learning leverages natural audio-visual correlations in videos.
- Self-supervised pre-training reduces reliance on human annotations for speech reconstruction.
Purpose of the Study:
- Investigate the effectiveness of crossmodal self-supervised pre-training for video-to-audio speech reconstruction.
- Develop a model (LipSound2) capable of mapping visual facial sequences to audio spectrograms.
Main Methods:
- Utilized an encoder-decoder architecture with a location-aware attention mechanism.
- Pre-trained LipSound2 on multilingual audio-visual data (VoxCeleb2).
- Fine-tuned the model on domain-specific English datasets (GRID, TCD-TIMIT) and Chinese datasets (CMLR).
Main Results:
- Achieved significant improvements in speech quality and intelligibility for English speech reconstruction.
- Demonstrated generalizability across speaker-dependent and independent settings.
- Showcased transferability for Chinese speech reconstruction and state-of-the-art performance in cascaded lip reading systems.
Conclusions:
- Crossmodal self-supervised pre-training is highly effective for speech reconstruction from video.
- LipSound2 offers a robust and adaptable solution for lip reading and speech recognition tasks.
- The approach advances the state-of-the-art in both English and Chinese speech-related AI applications.
