The goal of this work is to learn discriminative visual representations for lip reading without access to manual text annotation. Recent advances in cross-modal self-supervised learning have shown that the corresponding audio can serve as a supervisory signal to learn effective visual representations for lip reading. However, existing methods only exploit the natural synchronization of the video and the corresponding audio. We find that both video and audio are actually composed of speech-related information, identityr elated information, and modal information. To make the visual representations (i) more discriminative for lip reading and (ii) indiscriminate with respect to the identities and modals, we propose a novel self-supervised learning framework called Adversarial Dual-Contrast Self-Supervised Learning (ADC-SSL), to go beyond previous methods by explicitly forcing the visual representations disentangled from speech-unrelated information. Experimental results clearly show that the proposed method outperforms state-of-the-art cross-modal self-supervised baselines by a large margin. Besides, ADC-SSL can outperform its supervised counterpart without any finetune. CCS CONCEPTS• Computing methodologies → Computer vision; Speech recognition; Neural networks.

Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation. To push forward future research on visual speech, this paper aims to present a comprehensive review of recent progress in deep learning methods on visual speech analysis. We cover different aspects of visual speech, including fundamental problems, challenges, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. Besides, we also identify gaps in current research and discuss inspiring future research directions.

The goal of this work is to recognize words, phrases, and sentences being spoken by a talking face without given the audio. Current deep learning approaches for lip reading focus on exploring the appearance and optical flow information of videos. However, these methods do not fully exploit the characteristics of lip motion. In addition to appearance and optical flow, the mouth contour deformation usually conveys significant information that is complementary to others. However, the modeling of dynamic mouth contour has received little attention than that of appearance and optical flow. In this work, we propose a novel model of dynamic mouth contours called Adaptive Semantic-Spatio-Temporal Graph Convolution Network (ASST-GCN), to go beyond previous methods by automatically learning both the spatial and temporal information from videos. To combine the complementary information from appearance and mouth contour, a two-stream visual front-end network is proposed. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art lip reading methods on several large-scale lip reading benchmarks.

Lip reading is the task of decoding text from speakers' mouth movements. Numerous deep learning-based methods have been proposed to address this task. However, these existing deep lip reading models suffer from poor generalization due to overfitting the training data. To resolve this issue, we present a novel learning paradigm that aims to improve the interpretability and generalization of lip reading models. In specific, a Variational Temporal Mask (VTM) module is customized to automatically analyze the importance of frame-level features. Furthermore, the prediction consistency constraints of global information and local temporal important features are introduced to strengthen the model generalization. We evaluate the novel learning paradigm with multiple lip reading baseline models on the LRW and LRW-1000 datasets. Experiments show that the proposed framework significantly improves the generalization performance and interpretability of lip reading models.