Audiovisual speech recognition: A review and forecast. (22nd December 2020)

• Record Type: Journal Article
• Authors: Xia, Linlin; Chen, Gang; Xu, Xun; Cui, Jiashuo; Gao, Yiping
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 6(2020:Nov./Dec.)
• Publication Date: 2020-12-22
• Subjects: AVSR -- multimodality HCI -- public databases -- deep learning -- neural networks; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420976082
• Languages: English
• ISSNs: 1729-8806

Abstract

Audiovisual speech recognition is a favorable solution to multimodality human–computer interaction. For a long time, it has been very difficult to develop machines capable of generating or understanding even fragments of natural languages; the fused sight, smelling, touching, and so on provide machines with possible mediums to perceive and understand. This article presents a detailed review of recent advances in audiovisual speech recognition area. After explicitly representing audiovisual speech recognition development phase divided by timeline, we focus on typical audiovisual speech database descriptions in terms of single view and multi-view, since the public databases for general purpose should be the first concern for audiovisual speech recognition tasks. For the following challenges that are inseparably related to the feature extraction and dynamic audiovisual fusion, the principal usefulness of deep learning-based tools, such as deep fully convolutional neural network, bidirectional long short-term memory network, 3D convolutional neural network, and so on, lies in the fact that they are relatively simple solutions of such problems. As the principle analyses and comparisons related to computational load, accuracy, and applicability of well-developed audiovisual speech recognition frameworks have been conducted, we further illuminate our insights into the future audiovisual speech recognition architecture design. We argue that end-to-end audiovisual speech recognition model and deep learning-based feature extractors will guide multimodality human–computer interaction directly to a solution.