arXiv:2403.16071v2 Announce Type: replace-cross
Abstract: Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity changes, poses a challenging problem due to inter-speaker variability. A well-trained lip reading system may perform poorly when handling a brand new speaker. To learn a speaker-robust lip reading model, a key insight is to reduce visual variations across speakers, avoiding the model overfitting to specific speakers. In this work, in view of both input visual clues and latent representations based on a hybrid CTC/attention architecture, we propose to exploit the lip landmark-guided fine-grained visual clues instead of frequently-used mouth-cropped images as input features, diminishing speaker-specific appearance characteristics. Furthermore, a max-min mutual information regularization approach is proposed to capture speaker-insensitive latent representations. Experimental evaluations on public lip reading datasets demonstrate the effectiveness of the proposed approach under the intra-speaker and inter-speaker conditions.
Expert Commentary: Lip Reading in Cross-Speaker Scenarios
In recent years, lip reading has gained significant attention as a realistic and practical application in various fields. Deep learning approaches have revolutionized lip reading systems, but they still face challenges in cross-speaker scenarios where the speaker’s identity changes. This problem arises due to the inter-speaker variability, which makes it difficult for a well-trained lip reading system to handle new speakers effectively.
A crucial insight to overcome this challenge is to reduce visual variations across speakers in order to avoid overfitting the model to specific individuals. To achieve this goal, the authors of this work propose a novel approach that focuses on exploiting lip landmark-guided fine-grained visual clues instead of the commonly used mouth-cropped images as input features. By leveraging the lip landmarks, the system can effectively diminish speaker-specific appearance characteristics and capture more robust information for lip reading.
Furthermore, the authors introduce a max-min mutual information regularization approach to capture speaker-insensitive latent representations. This regularization technique helps the model capture shared characteristics among different speakers while suppressing speaker-specific information. By doing so, the system can generalize better to unseen speakers, resulting in improved performance in cross-speaker scenarios.
This work highlights the multi-disciplinary nature of the concepts involved in lip reading systems. It combines deep learning techniques, computer vision, and information theory to address the unique challenges of lip reading in cross-speaker scenarios. By integrating knowledge from multiple domains, the authors provide a comprehensive solution that tackles both the input visual clues and the latent representations to enhance the performance of lip reading systems.
From a broader perspective, this work is closely related to the field of multimedia information systems. Lip reading is a form of multimedia analysis that involves processing visual cues to extract meaningful information, in this case, speech. The proposed approach leverages deep learning techniques, which are widely used in multimedia information systems for tasks such as image and video analysis. By adapting these techniques to the specific challenges of lip reading, this work contributes to the advancement of multimedia information systems in the domain of speech analysis.
Additionally, the concepts and techniques discussed in this work have implications for other related fields such as animations, artificial reality, augmented reality, and virtual realities. Lip reading systems play a crucial role in enabling realistic and immersive interactions in these environments. The ability to accurately interpret silent speech can enhance the user experience and enable more natural communication in virtual and augmented reality applications. Therefore, the advancements in lip reading presented in this work can have a significant impact on the development of these technologies.
In conclusion, this work presents a novel approach to address the challenges of lip reading in cross-speaker scenarios. By leveraging lip landmark-guided visual clues and applying max-min mutual information regularization, the proposed system achieves improved performance in handling new speakers. The multi-disciplinary nature of the concepts and their relevance to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities make this work a valuable contribution to the lip reading research community.