On Dual View Lipreading Using High Speed Camera

A fundamental task in pattern recognition field is to find a suitable representation for a feature. In this paper, we present a new visual speech feature representation approach that combines Hypercolumn Model (HCM) with HMM to perform a complete lip-reading system. In this system, we use HCM to extract visual speech features from input image. The extracted features are modeled by Gaussian distributions through using HMM. The proposed lip-reading system can work under varying lip positions and sizes. All images were captured in a natural environment without using special lighting or lip markers. Experimental results are shown to compare favourably with the results of two reported systems: SOM and DCT base systems. HCM provides better performance than both systems.

For automatic lipreading, there are many competing methods for feature extraction. Often, because of the complexity of the task these methods are tested on only quite restricted datasets, such as the letters of the alphabet or digits, and from only a few speak- ers. In this paper we compare some of the leading methods for lip feature extraction and compare them on the GRID dataset which uses a constrained vocabulary over, in this case, 15 speakers. Previously the GRID data has had restricted attention because of the requirements to track the face and lips accurately. We overcome this via the use of a novel linear predictor (LP) tracker which we use to control an Active Appearance Model (AAM). By ignoring shape and/or appearance parameters from the AAM we can quantify the effect of appearance and/or shape when lip-reading. We find that shape alone is a useful cue for lip- reading (which is consistent with human experiments). However, the incremental effect of shape on appearance appears to be not significant which implies that the inner appearance of the mouth contains more information than the shape.

Proceedings of International Conference on Auditory-Visual Speech Processing, 2010

Automatic speech recognition systems that utilise the visual modality of speech often are investigated within a speakerdependent or a multi-speaker paradigm. That is, during training the recogniser will have had prior exposure to example speech from each of the possible test speakers. In a previous paper we highlighted the danger of not using different speakers in the training and test sets, and demonstrated that, within a speakerindependent configuration, lip-reading performance degrades dramatically due to ...

Proceedings of the AAAI Conference on Artificial Intelligence

Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the work in building lipreading systems has been limited to classifying silent videos into classes representing text phrases. However, there are multiple problems associated with making lipreading a text-based classification task like its dependence on a particular language and vocabulary mapping. Thus, in this paper we propose a multi-view lipreading to audio system, namely Lipper, which models it as a regression task. The model takes silent videos as input and produces speech as the output. With multi-view silent videos, we observe an improvement over single-view speech reconstruction results. We show this by presenting an exhaustive set of experiments for speaker-dependent, out-of-vocabulary and speaker-independent settings. Further, we compare the delay values of Lipper with other speechreading systems in order to show the real-time nature of audio p...

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021

Abstract. Visual information plays a key role in automatic speech recognition (ASR) when audio is corrupted by background noise, or even inaccessible. Speech recognition using visual information is called lip-reading. The initial idea of visual speech recognition comes from humans’ experience: we are able to recognize spoken words from the observation of a speaker's face without or with limited access to the sound part of the voice. Based on the conducted experimental evaluations as well as on analysis of the research field we propose a novel task-oriented approach towards practical lip-reading system implementation. Its main purpose is to be some kind of a roadmap for researchers who need to build a reliable visual speech recognition system for their task. In a rough approximation, we can divide the task of lip-reading into two parts, depending on the complexity of the problem. First, if we need to recognize isolated words, numbers or small phrases (e.g. Telephone numbers with ...

International Journal of Advanced Trends in Computer Science and Engineering, 2020

Studies show that only about 30 to 45 percent of English language can be understood by lipreading alone. Even the most talented lip readers are unable to collect a complete message based on lipreading only, although they are often very good at interpreting facial features, body language, and context to find out. As you can imagine, this technique affects the brain in different ways and becomes exhausting over a period of time. If a person who is deaf, uses language and is able to read lips, hearing people may not understand the challenges they are facing just to have a simple one-on-one conversation. The hearing person may be annoyed that they are often asked to repeat themselves or to speak more slowly and clearly. They could lose patience and break off the conversation. In our modern world, where technology connects us in a way never thought possible, there are a variety of ways to communicate with another person. Deaf people come from all walks of life and with different backgrounds. In this study, a lipreading model is being developed that is able to record, analyze, translate the movement of lips and display them into subtitles. A model is trained with GRID Corpus, MIRACL-VC1 and pre-trained dataset and with the LipNet model to build a system which deaf people can decode text from the movement of a speaker's mouth. This system will help the deaf people understand what others are actually saying and communicate more effectively. As a conclusion, this system helps deaf people to communicate effectively with others.

Lip reading is used to understand or interpret speech without hearing it, a technique especially mastered by people with hearing difficulties. The ability to lip read enables a person with a hearing impairment to communicate with others and to engage in social activities, which otherwise would be difficult. Recent advances in the fields of computer vision, pattern recognition, and signal processing has led to a growing interest in automating this challenging task of lip reading. Indeed, automating the human ability to lip read, a process referred to as visual speech recognition, could open the door for other novel applications. This thesis investigates various issues faced by an automated lip-reading system and proposes a novel “visual words” based approach to automatic lip reading. The proposed approach includes a novel automatic face localisation scheme and a lip localisation method. The traditional approaches to automatic lip reading are based on visemes (mouth shapes (or appeara...

Proceedings - International Conference on Pattern Recognition, 2006

Speech recognition solely based on visual information such as the lip shape and its movement is referred to as lipreading. This paper presents an automatic lipreading technique for speaker dependent (SD) and speaker independent (SI) speech recognition tasks. Since the visual features are derived according to the frame rate of the video sequence, spline representation is then employed to translate the discrete-time sampled visual features into continuous domain. The spline coefficients in the same word class are constrained to have similar expression and can be estimated from the training data by the EM algorithm. In addition, an adaptive multi-model approach is proposed to overcome the variation caused by different speaking style in speaker-independent recognition task. The experiments are carried out to recognize the ten English digits and an accuracy of 96% for speaker dependent recognition and 88% for speaker independent recognition have been achieved, which shows the superiority of our approach compared with other classifiers investigated.

Applied Sciences

Lipreading is understanding speech from observed lip movements. An observed series of lip motions is an ordered sequence of visual lip gestures. These gestures are commonly known, but as yet are not formally defined, as `visemes’. In this article, we describe a structured approach which allows us to create speaker-dependent visemes with a fixed number of visemes within each set. We create sets of visemes for sizes two to 45. Each set of visemes is based upon clustering phonemes, thus each set has a unique phoneme-to-viseme mapping. We first present an experiment using these maps and the Resource Management Audio-Visual (RMAV) dataset which shows the effect of changing the viseme map size in speaker-dependent machine lipreading and demonstrate that word recognition with phoneme classifiers is possible. Furthermore, we show that there are intermediate units between visemes and phonemes which are better still. Second, we present a novel two-pass training scheme for phoneme classifiers....

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007

In this paper, we introduce profile view (PV) lip reading, a scheme for speaker-dependent isolated word speech recognition. We provide historic motivation for PV from the importance of profile images in facial animation for lip reading, and we present feature extraction schemes for PV as well as for the traditional frontal view (FV) approach. We compare lip reading results for PV and FV, which demonstrate a significant improvement for PV over FV. We show improvement in speech recognition with the integration of Audio and Visual features. We also found it advantageous to process the visual features over a longer duration than the duration marked by the endpoints of the speech utterance.