Visual Speech Recognition (VSR) is the task of predicting spoken words from
silent lip movements. VSR is regarded as a challenging task because of the
insufficient information on lip movements. In this paper, we propose an Audio
Knowledge empowered Visual Speech Recognition framework (AKVSR) to complement
the insufficient speech information of visual modality by using audio modality.
Different from the previous methods, the proposed AKVSR 1) utilizes rich audio
knowledge encoded by a large-scale pretrained audio model, 2) saves the
linguistic information of audio knowledge in compact audio memory by discarding
the non-linguistic information from the audio through quantization, and 3)
includes Audio Bridging Module which can find the best-matched audio features
from the compact audio memory, which makes our training possible without audio
inputs, once after the compact audio memory is composed. We validate the
effectiveness of the proposed method through extensive experiments, and achieve
new state-of-the-art performances on the widely-used LRS3 dataset.
Visual Speech Recognition (VSR) is a significant area of research within the field of multimedia information systems, as it involves the analysis and understanding of silent lip movements to predict spoken words. This task is particularly challenging due to the limited amount of information available solely from visual cues.
In this paper, the authors propose a novel framework called Audio Knowledge empowered Visual Speech Recognition (AKVSR) to address the limitations of existing methods. The key idea behind AKVSR is to leverage audio modality to complement the insufficient speech information provided by visual cues.
The authors introduce several unique components in the AKVSR framework that contribute to its effectiveness. Firstly, they utilize a large-scale pretrained audio model to encode rich audio knowledge. By leveraging this pretrained model, the framework is able to benefit from the linguistic information contained in the audio domain.
Secondly, the authors introduce a technique called quantization to save the linguistic information of audio knowledge in a compact audio memory. This involves discarding non-linguistic information from the audio, resulting in a more efficient representation that can be easily accessed during training.
Finally, the AKVSR framework incorporates an Audio Bridging Module, which plays a crucial role in finding the best-matched audio features from the compact audio memory. This module ensures that the training process can proceed even without audio inputs, once the compact audio memory has been composed.
The proposed AKVSR framework is evaluated extensively on the LRS3 dataset, a widely-used benchmark for VSR tasks. The experiments demonstrate that the framework achieves new state-of-the-art performances, indicating its effectiveness in leveraging audio knowledge for visual speech recognition.
From a multidisciplinary perspective, this research brings together concepts from various fields such as computer vision, speech recognition, and machine learning. By combining knowledge and techniques from these domains, the authors address the challenges associated with visual speech recognition and propose a novel approach that pushes the boundaries of performance.
The findings of this research have implications beyond VSR. The concept of leveraging multimodal information (in this case, audio and visual) to enhance the performance of a system can be applied to a wide range of multimedia information systems. This includes areas such as animations, artificial reality, augmented reality, and virtual realities, where integrating multiple sensory modalities can lead to more immersive and realistic experiences.
In summary, the proposed AKVSR framework demonstrates the power of leveraging audio knowledge to complement visual cues in the task of visual speech recognition. This research contributes to the broader field of multimedia information systems, highlighting the importance of incorporating multimodal approaches for enhanced performance in various applications.