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Automatic visual lipreading, an efficient and convenient way of human–machine interaction, recognizes the content of the conversation from dynamic visual features of the speakers. Automatic lipreading based on acoustic speech alone can effectively prevent interference in complex environment, particularly under noisy conditions. In this paper, we propose novel visual extraction and HMM classification methods for automatic lipreading system, which reduce the dimension by using locally sensitive discriminant analysis algorithm and quantitative cluster by K-means algorithm. A model-based hybrid feature extraction method is proposed by optimizing the weight matrix of the LSDA algorithm. The effectiveness of the suggested approach is demonstrated by preliminary experiments on the English video database. Experimental results demonstrate that the proposed optimized algorithm can increase recognition rate up to 97%, which is 18% higher than the original algorithm. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has already been applied to English lip-reading. However, Chinese lip-reading starts late and lacks relevant dataset, and the recognition accuracy is not ideal. Therefore, this paper proposes a new hybrid neural network model to establish a Chinese lip-reading system. In this paper, we integrate the attention mechanism into both CNN and RNN. Specifically, we add the convolutional block attention module (CBAM) to the ResNet50 neural network, which enhances its ability to capture the small differences among the mouth patterns of similarly pronounced words in Chinese, improving the performance of feature extraction in the convolution process. We also add the time attention mechanism to the GRU neural network, which helps to extract the features among consecutive lip motion images. Considering the effects of the moments before and after on the current moment in the lip-reading process, we assign more weights to the key frames, which makes the features more representative. We further validate our model through experiments on our self-built dataset. Our experiments show that using convolutional block attention module (CBAM) in the Chinese lip-reading model can accurately recognize Chinese numbers 0–9 and some frequently used Chinese words. Compared with other lip-reading systems, our system has better performance and higher recognition accuracy. [ABSTRACT FROM AUTHOR]

Traditional automatic lip-reading systems generally consist of two stages: feature extraction and recognition, while the handcrafted features are empirical and cannot learn the relevance of lip movement sequence sufficiently. Recently, deep learning approaches have attracted increasing attention, especially the significant improvements of convolution neural network (CNN) applied to image classification and long short-term memory (LSTM) used in speech recognition, video processing and text analysis. In this paper, we propose a hybrid neural network architecture, which integrates CNN and bidirectional LSTM (BiLSTM) for lip reading. First, we extract key frames from each isolated video clip and use five key points to locate mouth region. Then, features are extracted from raw mouth images using an eight-layer CNN. The extracted features have the characteristics of stronger robustness and fault-tolerant capability. Finally, we use BiLSTM to capture the correlation of sequential information among frame features in two directions and the softmax function to predict final recognition result. The proposed method is capable of extracting local features through convolution operations and finding hidden correlation in temporal information from lip image sequences. The evaluation results of lip-reading recognition experiments demonstrate that our proposed method outperforms conventional approaches such as active contour model (ACM) and hidden Markov model (HMM). [ABSTRACT FROM AUTHOR]

• Lip reading is a novel and intelligent way of human-computer interaction. • Quad-tree Markov random field based unsupervised lip segmentation is proposed. • Multi-scale characteristics of wavelet transform is applied to MRF segmentation. • Better lip segmentation accuracy is achieved compared to other MRF-based methods. In this paper, we propose a new extended method to implement lip segmentation. Based on a quad-tree structure (QTS) Markov random field (MRF) model under unsupervised situation, the proposed method achieves the lip segmentation in wavelet domain. Firstly, we set up a multi-layer hierarchical model, in which each pixel of every layer corresponds to the four nodes in quad-tree structure. Then the probability of a branch node can be calculated by using the probability of the previous branch node through the tree structure easily. Subsequently, a Markov random field derived from the model is obtained so that the segmentation problem is formulated as a labeling optimization problem in the framework of the maximum a posteriori Markov random field (MAP-MRF). Assuming that the pre-assigned cluster of data segments may overestimate the underlying fact, and leads to over-segmentation, we propose a variable-weight segmentation approach to improve the robustness of the segmentation. The experimental results show that this method has better segmentation accuracy than traditional methods. [ABSTRACT FROM AUTHOR]

As a significant component of the Human Computer Interface (HCI), automatic lip reading is designed for the purpose of understanding the content of speech by interpreting the movements of the lips. Although performance of automatic lip reading system is easily affected by challenging conditions such as noise, illumination and low resolution, enormous advancements in the relevant fields accompanied with enhancement in computer capability have improved the robustness of the system, making it more adaptable to the real environment. In this paper, we study the field and gives a detailed discussion on the actuality and the developing level of automatic lip reading. We emphatically introduce the feature extraction and recognition model algorithms. We also compare and analyze the various visual speech databases for their characteristics and functions in speech recognition systems. In addition, we describe the challenges and offer our insights into future research direction of automatic lip reading. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour. [ABSTRACT FROM AUTHOR]