Lip-reading with Densely Connected Temporal Convolutional Networks

In this work, we present the Densely Connected Temporal Convolutional Network (DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional Networks (TCN) have recently demonstrated great potential in many vision tasks, its receptive fields are not dense enough to model the complex temporal dynamics in lip-reading scenarios. To address this problem, we introduce dense connections into the network to capture more robust temporal features. Moreover, our approach utilises the Squeeze-and-Excitation block, a light-weight attention mechanism, to further enhance the model's classification power. Without bells and whistles, our DC-TCN method has achieved 88.36% accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the LRW-1000 dataset, which has surpassed all the baseline methods and is the new state-of-the-art on both datasets.Comment: WACV 202

Audio-Visual Automatic Speech Recognition Towards Education for Disabilities

Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition

Applications of Face Analysis and Modeling in Media Production

Facial expressions play an important role in day-by-day communication as well as media production. This article surveys automatic facial analysis and modeling methods using computer vision techniques and their applications for media production. The authors give a brief overview of the psychology of face perception and then describe some of the applications of computer vision and pattern recognition applied to face recognition in media production. This article also covers the automatic generation of face models, which are used in movie and TV productions for special effects in order to manipulate people's faces or combine real actors with computer graphics

End-to-end Lip-reading: A Preliminary Study

Deep lip-reading is the combination of the domains of computer vision and natural language processing. It uses deep neural networks to extract speech from silent videos. Most works in lip-reading use a multi staged training approach due to the complex nature of the task. A single stage, end-to-end, unified training approach, which is an ideal of machine learning, is also the goal in lip-reading. However, pure end-to-end systems have not yet been able to perform as good as non-end-to-end systems. Some exceptions to this are the very recent Temporal Convolutional Network (TCN) based architectures. This work lays out preliminary study of deep lip-reading, with a special focus on various end-to-end approaches. The research aims to test whether a purely end-to-end approach is justifiable for a task as complex as deep lip-reading. To achieve this, the meaning of pure end-to-end is first defined and several lip-reading systems that follow the definition are analysed. The system that most closely matches the definition is then adapted for pure end-to-end experiments. Four main contributions have been made: i) An analysis of 9 different end-to-end deep lip-reading systems, ii) Creation and public release of a pipeline1 to adapt sentence level Lipreading Sentences in the Wild 3 (LRS3) dataset into word level, iii) Pure end-to-end training of a TCN based network and evaluation on LRS3 word-level dataset as a proof of concept, iv) a public online portal2 to analyse visemes and experiment live end-to-end lip-reading inference. The study is able to verify that pure end-to-end is a sensible approach and an achievable goal for deep machine lip-reading

Statistical Lip-Appearance Models Trained Automatically Using Audio Information

We aim at modeling the appearance of the lower face region to assist visual feature extraction for audio-visual speech processing applications. In this paper, we present a neural network based statistical appearance model of the lips which classifies pixels as belonging to the lips, skin, or inner mouth classes. This model requires labeled examples to be trained, and we propose to label images automatically by employing a lip-shape model and a red-hue energy function. To improve the performance of lip-tracking, we propose to use blue marked-up image sequences of the same subject uttering the identical sentences as natural nonmarked-up ones. The easily extracted lip shapes from blue images are then mapped to the natural ones using acoustic information. The lip-shape estimates obtained simplify lip-tracking on the natural images, as they reduce the parameter space dimensionality in the red-hue energy minimization, thus yielding better contour shape and location estimates. We applied the proposed method to a small audio-visual database of three subjects, achieving errors in pixel classification around 6%, compared to 3% for hand-placed contours and 20% for filtered red-hue