A survey on automated lip-reading approaches is presented in this paper with the main focus being on deep learning related methodologies which have proven to be more fruitful for both feature extraction and classification. This survey also provides comparisons of all the different components that make up automated lip-reading systems including the audio-visual databases, feature extraction, classification networks and classification schemas. The main contributions and unique insights of this survey are: 1) A comparison of Convolutional Neural Networks with other neural network architectures for feature extraction; 2) A critical review on the advantages of Attention-Transformers and Temporal Convolutional Networks to Recurrent Neural Networks for classification; 3) A comparison of different classification schemas used for lip-reading including ASCII characters, phonemes and visemes, and 4) A review of the most up-to-date lip-reading systems up until early 2021.

In this paper, a neural network-based lip reading system is proposed. The system is lexicon-free and uses purely visual cues. With only a limited number of visemes as classes to recognise, the system is designed to lip read sentences covering a wide range of vocabulary and to recognise words that may not be included in system training. The system has been testified on the challenging BBC Lip Reading Sentences 2 (LRS2) benchmark dataset. Compared with the state-of-the-art works in lip reading sentences, the system has achieved a significantly improved performance with 15% lower word error rate. In addition, experiments with videos of varying illumination have shown that the proposed model has a good robustness to varying levels of lighting. The main contributions of this paper are: 1) The classification of visemes in continuous speech using a specially designed transformer with a unique topology; 2) The use of visemes as a classification schema for lip reading sentences; and 3) The conversion of visemes to words using perplexity analysis. All the contributions serve to enhance the accuracy of lip reading sentences. The paper also provides an essential survey of the research area.

As an alternative approach, viseme-based lipreading systems have demonstrated promising performance results in decoding videos of people uttering entire sentences. However, the overall performance of such systems has been significantly affected by the efficiency of the conversion of visemes to words during the lipreading process. As shown in the literature, the issue has become a bottleneck of such systems where the system’s performance can decrease dramatically from a high classification accuracy of visemes (e.g., over 90%) to a comparatively very low classification accuracy of words (e.g., only just over 60%). The underlying cause of this phenomenon is that roughly half of the words in the English language are homophemes, i.e., a set of visemes can map to multiple words, e.g., “time” and “some”. In this paper, aiming to tackle this issue, a deep learning network model with an Attention based Gated Recurrent Unit is proposed for efficient viseme-to-word conversion and compared against three other approaches. The proposed approach features strong robustness, high efficiency, and short execution time. The approach has been verified with analysis and practical experiments of predicting sentences from benchmark LRS2 and LRS3 datasets. The main contributions of the paper are as follows: (1) A model is developed, which is effective in converting visemes to words, discriminating between homopheme words, and is robust to incorrectly classified visemes; (2) the model proposed uses a few parameters and, therefore, little overhead and time are required to train and execute; and (3) an improved performance in predicting spoken sentences from the LRS2 dataset with an attained word accuracy rate of 79.6%—an improvement of 15.0% compared with the state-of-the-art approaches.

The performance of automated lip reading using visemes as a classification schema has achieved less success compared with the use of ASCII characters and words largely due to the problem of different words sharing identical visemes. The Generative Pre-Training transformer is an effective autoregressive language model used for many tasks in Natural Language Processing, including sentence prediction and text classification. This paper proposes a new application for this model and applies it in the context of lip reading, where it serves as a language model to convert visual speech in the form of visemes, to language in the form of words and sentences. The network uses the search for optimal perplexity to perform the viseme-to-word mapping and is thus a solution to the one-to-many mapping problem that exists whereby various words that sound different when spoken look identical. This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known. Sentences from the benchmark BBC dataset "Lip Reading Sentences in the Wild"(LRS2), are classified with a character error rate of 10.7% and a word error rate of 18.0%. The main contribution of this paper is to propose a method of predicting words through the use of perplexity analysis when only visual cues are present, using an autoregressive language model.