Learning visual speech representations from talking face videos is an important problem for several speech-related tasks, such as lip reading, talking face generation, audio-visual speech separation, and so on. The key difficulty lies in tackling speech-irrelevant factors presented in the videos, such as lighting, resolution, viewpoints, head motion, and so on.
To solve the problems, a research team led by Shuang YANG publishes their new research on 15 December 2024 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposes to disentangle speech-relevant and speech-irrelevant facial movements from videos in a self-supervised learning manner. The proposed method can learn discriminative disentangled speech representations from videos and can benefit the lip reading task by a straightforward method like knowledge distillation. Both qualitative and quantitative results on the popular visual speech datasets LRW and LRS2-BBC show the effectiveness of their method.
In the research, the researchers observe the speech process and find that speech-relevant and speech-irrelevant facial movements are differences in the frequency of occurrence. Specifically, speech-relevant facial movements always occur at a higher frequency than speech-irrelevant ones. Moreover, the researchers find that the speech-relevant facial movements are consistently synchronized with the accompanying audio speech signal.
Based on the new observations above, the researchers introduce a novel two-branch network to decompose the visual changes between two frames in the same video into speech-relevant and speech-irrelevant components. For speech-relevant branch, they introduce the high-frequency audio signal to guide the learning of speech-relevant cues. For the speech-irrelevant branch, they introduce an information bottleneck to restrict the capacity from acquiring high-frequency and fine-grained speech-relevant information.
Future work can focus on exploring more explicit auxiliary tasks and constraints beyond the reconstruction task to capture speech cues from videos. Meanwhile, it's also a nice try to combine multiple types of knowledge representations to enhance the obtained speech representations.
DOI: 10.1007/s11704-024-3787-8
Frontiers of Computer Science
Experimental study
Not applicable
Audio-guided self-supervised learning for disentangled visual speech representations
15-Dec-2024