A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading

Detection of meaningful meeting events is very important for cross-modal analysis of planning meetings. Many important events are related to speaker's communication behavior. In visual-audio based speaker detection, mouth positions and movements are needed as visual information. We present our techniques to detect mouth positions and movements of a talking person in meetings. First, we build a skin color model with the Gaussian distribution. After training with skin color samples, we obtain parameters for the model. A skin color filter is created corresponding to the model with a threshold. We detect face regions for all participants in the meeting. Second, We create a mouth template and perform image matching to find candidates of the mouth in each face region. Next, according to the fact that the skin color in lip areas is different from other areas in the face region, by comparing dissimilarities of skin color between candidates and the original color model, we decide the mouth area from the candidates. Finally, we detect mouth movements by computing normalized cross-correlation coefficients of mouth area between two successive frames. A real-time system has been implemented to track speaker's mouth positions and detection mouth movements. Applications also include video conferencing and improving human computer interaction (HCI). Examples in meeting environments and others are provided.

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem - unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available.

The effects of syntactic and information structures on sentence processing load were investigated using two reading comprehension experiments in Japanese, a head-final SOV language. In the first experiment, we discovered the main effects of syntactic and information structures, as well as their interaction, showing that interaction of these two factors is not restricted to head-initial languages. The second experiment revealed that the interaction between syntactic structure and information structure occurs at the second NP (O of SOV and S of OSV), which, crucially, is a pre-head position, suggesting the incremental nature of the processing of both syntactic structure and information structure in head-final languages.

This paper focuses on rising contours in English read speech. Our hypothesis is that they are very few in this particular speech style. This is confirmed by quantitative and qualitative analyses, conducted on a corpus of read speech by native English speakers with a standard British English accent. The main result of the quantitative analyses is that out of 1076 tone units, 82% (whether final or not) are uttered with a falling contour, which is much more than could be expected. The qualitative analyses consisted in a thorough examination of the intonation contours in relation with the syntactic characteristics of our data, as well as an analysis of the pragmatic functions of the contours. They allow us to revisit the generally accepted idea that falling contours are associated with final statements and rises with yes-no questions and continuation. We show that the tonal sequence fall plus fall is by far the most common in read speech, whatever the syntactic structure, except for enumerations. Contrary to what is stated in the literature, the main function of rising contours is not to indicate non-finality and continuation, but rather to convey attitudes, at least in read speech.

Lip reading is the task of decoding and understanding speech from the movement of a speaker's mouth. This can be extremely beneficial for aiding the hearing impaired to ‘listen’ to people who do not know sign language in real-world environments with a lot of noise pollution. Orthodoxically methods have focused mainly on heavy preprocessing. Despite showing tremendous potential, application of deep learning algorithms has been limited in this field. Here we present a convolutional neural network model to predict words from videos without any audio. It is developed using the pre-trained deep learning architecture VGG Net, pre-trained on the ImageNet Database with some custom modifications on the MIRACL-VCl Dataset of 10 words. The model achieved an accuracy of 94.86% in training, 93.82% in validation and 60% in testing. An app has been developed using this model which can use cloud computing to run the model real time in any smartphone to aid the hearing-impaired in their day-to-day activities and can make conversations with hearing impaired people more natural, organic as well as cost friendly.

This paper proposes a multi-modal speech recognition method using optical-flow analysis for lip images. Optical flow is defined as the distribution of apparent velocities in the movement of brightness patterns in an image. Since the optical flow is computed without extracting the speaker's lip contours and location, robust visual features can be obtained for lip movements. Our method calculates two kinds of visual feature sets in each frame. The first feature set consists of variances of vertical and horizontal components of optical-flow vectors. These are useful for estimating silence/pause periods in noisy conditions since they represent movement of the speaker's mouth. The second feature set consists of maximum and minimum values of integral of the optical flow. These are expected to be more effective than the first set since this feature set has not only silence/pause information but also open/close status of the speaker's mouth. Each of the feature sets is combined with an acoustic feature set in the framework of HMM-based recognition. Triphone HMMs are trained using the combined parameter sets extracted from clean speech data. Noise-corrupted speech recognition experiments have been carried out using audio-visual data from 11 male speakers uttering connected digits. The following improvements of digit accuracy over the audio-only recognition scheme have been achieved when the visual information was used only for silence HMM: 4% at SNR = 5 dB and 13% at SNR = 10 dB using the integral information of optical flow as the visual feature set.

Lip reading, the process of interpreting speech by visually observing the movements of the lips, has emerged as a critical area of research with applications spanning communication aids for the hearing impaired, silent speech interfaces, and enhanced human-computer interaction. This paper reviews recent advancements in lip reading technologies, focusing on the integration of machine learning and computer vision techniques. We explore state-of-the-art methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models that have significantly improved the accuracy and robustness of lip reading systems. The study highlights the importance of large annotated datasets, such as LipNet and LRW, which have facilitated the training of deep learning models. Additionally, we examine multimodal approaches that combine visual information with audio signals to enhance performance, especially in noisy environments. Despite substantial progress, challenges remain in addressing speaker variability, low resolution, and real-time processing. Future research directions are discussed, emphasizing the need for more diverse datasets, improved model generalization, and real-world application testing. This comprehensive review underscores the potential of advanced lip reading technologies to revolutionize communication accessibility and human-computer interaction. This paper presents the method for Vision based Lip Reading system that uses convolutional neural network (CNN) with attention-based Long Short-Term Memory (LSTM). The dataset includes video clips pronouncing words sentence. The pretrained CNN is used for extracting features from pre- processed video frames which then are processed for learning temporal characteristics by LSTM. The SoftMax layer of architecture provides the result of lip reading. In the present work experiments are performed with two pre-trained models. The system provides 80% accuracy using Tensorflow and ensemble learning. Keywords— CNN; RNN; LSTM; Tensorflow; lip reading; deep learning

The research aims are to find out the concept of semantics constructed in the “ Massure” poetry and to find out the meaning of “Massure” poetry by Muskamal Bare in city of Bone, South Sulawesi. In this study, the writer used descriptive qualitative method to analyzed data. The writer observed the location, interviewed to the author and some people in Bone, South Sulawesi , and used some tools as documentation which relate to the research in Bone, South Sulawesi.“Massure“ poetry has a lexical meaning and grammatical meaning. Lexical meaning that is the meaning of each word and grammatical meaning that is the meaning of each sentence. So there are differences among lexical and grammatical meaning that contained in “ Massure” poetry by Muskamal Bare in Bone , South Sulawesi.“ Massure “ is the delivery or coveying a story Either it is about the kingdom ,hero, natural disasters and etc. In this poetry talking about customs in the kingdom of Bone. Anyone who violates customs as good as he makes all people angry and been a disaster by many people.

: This research focuses on the lexical and grammatical meanings in the lyrics of Keisya Levronka's song "Tak Lagi Usai". This study uses a semantic approach. The purpose of this study is to first explain the grammatical meaning in the lyrics of the song "Tak Lagi Usai" by Keisya Levronka. The second explains the lexical meaning in the lyrics of Keisya Levronka's song "Unwanted to End". The research method used is descriptive qualitative method. This research is descriptive qualitative which means using interpretation (interpretative). The source of the data used is the song "Unwanted to End" by Keisya Levronka. The data analysis technique used is note-taking, namely by analyzing the grammatical and lexical meanings contained in the lyrics of the song "Tak Lagi Usai" by Keisya Levronka. The data in this study are all words, phrases, clauses, and sentences contained in the lyrics of Keisya Levronka's song "Tak Lagi Usai" which contain grammatical and lexical meanings. The results of the research conducted by the researcher concluded that there were 40 data, namely the first data of grammatical meaning, there were 22 data. Then the second data about lexical meaning there are 18 data.

Recognizing speech from silent lip movement, which is called lip reading, is a challenging task due to 1) the inherent information insufficiency of lip movement to fully represent the speech, and 2) the existence of homophenes that have similar lip movement with different pronunciations. In this paper, we try to alleviate the aforementioned two challenges in lip reading by proposing a Multi-head Visual-audio Memory (MVM). Firstly, MVM is trained with audio-visual datasets and remembers audio representations by modelling the inter-relationships of paired audio-visual representations. At the inference stage, visual input alone can extract the saved audio representation from the memory by examining the learned inter-relationships. Therefore, the lip reading model can complement the insufficient visual information with the extracted audio representations. Secondly, MVM is composed of multi-head key memories for saving visual features and one value memory for saving audio knowledge, which is designed to distinguish the homophenes. With the multi-head key memories, MVM extracts possible candidate audio features from the memory, which allows the lip reading model to consider the possibility of which pronunciations can be represented from the input lip movement. This also can be viewed as an explicit implementation of the one-to-many mapping of viseme-to-phoneme. Moreover, MVM is employed in multi-temporal levels to consider the context when retrieving the memory and distinguish the homophenes. Extensive experimental results verify the effectiveness of the proposed method in lip reading and in distinguishing the homophenes.

Lip reading is a complex but interesting path for the growth of speech recognition algorithms. It is the ability of deciphering spoken words by evaluating visual cues from lip movements. In this study, we suggest a unique method for lip reading that converts lip motions into textual representations by using deep neural networks. Convolutional neural networks are used in the methodology to extract visual features, recurrent neural networks are used to simulate temporal context, and the Connectionist Temporal Classification loss function is used to align lip features with corresponding phonemes. The study starts with a thorough investigation of data loading methods, which include alignment extraction and video preparation. A well selected dataset with video clips and matching phonetic alignments is presented. We select relevant face regions, convert frames to grayscale, then standardize the resulting data so that it can be fed into a neural network. The neural network architecture is presented in depth, displaying a series of bidirectional LSTM layers for temporal context understanding after 3D convolutional layers for spatial feature extraction. Careful consideration of input shapes, layer combinations, and parameter selections forms the foundation of the model's design. To train the model, we align predicted phoneme sequences with ground truth alignments using the CTC loss. Dynamic learning rate scheduling and a unique callback mechanism for training visualization of predictions are integrated into the training process. After training on a sizable dataset, the model exhibits remarkable convergence and proves its capacity to understand intricate temporal correlations. Through the use of both quantitative and qualitative evaluations, the results are thoroughly assessed. We visually check the model's lip reading abilities and assess its performance using common speech recognition criteria. It is explored how different model topologies and hyperparameters affect performance, offering guidance for future research. The trained model is tested on external video samples to show off its practical application. Its accuracy and resilience in lip-reading spoken phrases are demonstrated. By providing a deep learning framework for precise and effective speech recognition, this research adds to the rapidly changing field of lip reading devices. The results offer opportunities for additional development and implementation in various fields, such as assistive technologies, audio-visual communication systems, and human-computer interaction.

The paper presents empirical evidence for six reconstruction effects in Mandarin Chinese. It includes data concerning the copula verb, epistemic modals, scope of aspectual sentence-final particle –le over indefinite wh-phrases, downward entailing quantifiers, predicates of creation, Binding Condition B, quantifier scope and dōu-quantification. The syntactic structures in which reconstruction applies can be derived either by A- or A-bar-movement. Our investigation brings empirical facts to bear on the syntax-semantics interface showing an essential property of natural language that semantic properties and syntactic structures do not vary freely.

Virtual Reality (VR) is a kind of interactive experience technology. Human vision, hearing, expression, voice and even touch can be added to the interaction between humans and machine. Lip reading recognition is a new technology in the field of human-computer interaction, which has a broad development prospect. It is particularly important in a noisy environment and within the hearing- impaired population and is obtained by means of visual information from a video to make up for the deficiency of voice information. This information is a visual language that benefits from Augmented Reality (AR). The purpose is to establish an efficient and convenient way of communication. However, the traditional lip reading recognition system has high requirements of running speed and performance of the equipment because of its long recognition process and large number of parameters, so it is difficult to meet the requirements of practical application. In this paper, the mobile end lip-reading recognition system based on Raspberry Pi is implemented for the first time, and the recognition application has reached the latest level of our research. Our mobile lip-reading recognition system can be divided into three stages: First, we extract key frames from our own independent database, and then use a multi-task cascade convolution network (MTCNN) to correct the face, so as to improve the accuracy of lip extraction. In the second stage, we use MobileNets to extract lip image features and long short-term memory (LSTM) to extract sequence information between key frames. Finally, we compare three lip reading models: (1) The fusion model of Bi-LSTM and AlexNet. (2) A fusion model with attention mechanism. (3) The LSTM and MobileNets hybrid network model proposed by us. The results show that our model has fewer parameters and lower complexity. The accuracy of the model in the test dataset is 86.5%. Therefore, our mobile lip reading system is simpler and smaller than other PC platforms and saves computing resources and memory space.

The study of meaning in Arabic has developed within two major traditions: fiqh lughah as a classical linguistic heritage and Arabic semantics as a modern linguistic discipline. However, a systematic integration of both traditions into a comprehensive analytical framework encompassing lexical, grammatical, and contextual meanings remains underdeveloped. This article aims to: (1) explain the epistemological relationship between fiqh lughah and Arabic semantics; (2) analyze the three dimensions of meaning in Arabic; and (3) formulate an integrative framework applicable to the interpretation of Arabic texts, particularly religious texts. This study employs a qualitative descriptive approach using library research design, drawing on relevant classical and contemporary sources. The findings reveal that fiqh lughah and Arabic semantics are conceptually interconnected in understanding meaning. Lexical meaning provides the foundational layer through root systems and derivational patterns; grammatical meaning narrows interpretation through i‘rab and syntactic structure; while contextual meaning determines the final interpretation through situational, cultural, and historical analysis. The integration of these three dimensions forms a gradual and circular meaning-construction model that enhances interpretative accuracy in Arabic texts.

This paper presents a novel Convolutional Based Temporal Attention (CBTA) module that improves the performance of temporal convolutional networks (TCN) in lipreading tasks without requiring any additional data.Our CBTA method enhances recognition accuracy by focusing attention on relevant frames in the time sequence of a video.The study demonstrates how our strategy achieves groundbreaking sucacess on the Lip Reading in the Wild (LRW) dataset, achieving an accuracy of 92.61%-surpassing contemporary methods by approximately 0.5%.The experiment also indicates the broad utility and effectiveness of the adaptable CBTA.The proposed module substantially boosts Top-1 Accuracy for challenging words, offering a promising direction for overall performance improvement in lip-reading tasks.