Closed captioning

About: Closed captioning is a research topic. Over the lifetime, 3011 publications have been published within this topic receiving 64494 citations. The topic is also known as: CC.

Closed Captioned Telephone and Computer System

Abstract: A Closed Caption Telephony Portal (CCTP) computer system that provides real-time online telephony services that include utilizing speech recognition technology to extend telephone communication through closed captioning services to all incoming and outgoing phone calls. Phone calls are call forwarded to the CCTP system using services provided by a telephone carrier. The CCTP system is completely transportable and can be utilized on any computer system, Internet connection, and standard Internet Browser. Employing an HTML/Java based desktop interface, the CCTP system enables users to make and receive telephone calls, receive closed captioning of conversations, provide voice dialing and voice driven telephone functionality. Additional features allow call hold, call waiting, caller id, and conference calling. To use the CCTP system a user logs in with his or her username and password and this process will immediately set up a Virtual Private Network (VPN) between the client computer and the server.

An Integrated Hybrid CNN–RNN Model for Visual Description and Generation of Captions

Abstract: Video captioning is currently considered to be one of the simplest ways to index and search data efficiently. In today’s era, suitable captioning of video images can be facilitated with deep learning architectures. The focus of past research has been on providing image captions; however, the generation of high-quality captions with suitable semantics for different scenes has not yet been achieved. Therefore, this work aims to generate well-defined and meaningful captions to images and videos by using convolutional neural networks (CNN) and recurrent neural networks in combination. Beginning with the available dataset, features of images and videos were extracted using CNN. The extracted feature vectors were then utilized to generate a language model with the involvement of long short-term memory for individual word grams. The generated meaningful captions were trained using a softmax function, for performance computation using some predefined evaluation metrics. The obtained experimental results demonstrate that the proposed model outperforms existing benchmark models.

A Fine-Grained Spatial-Temporal Attention Model for Video Captioning

Abstract: Attention mechanism has been extensively used in video captioning tasks, which enables further development of deeper visual understanding. However, most existing video captioning methods apply the attention mechanism on the frame level, which only model the temporal structure and generated words, but ignore the region-level spatial information that provides accurate visual features corresponding to the semantic content. In this paper, we propose a fine-grained spatial-temporal attention model (FSTA), and the spatial information of objects appearing in the video will be our main concern. In the proposed FSTA, we achieve the spatial-hard attention at a fine-grained region level of objects through the mask pooling module and compute the temporal soft attention by using a two-layer LSTM network with attention mechanism to generate sentences. We test the proposed model on two benchmark datasets, namely, MSVD and MSR-VTT. The results indicate that our proposed FSTA model can achieve competitive performance against the state of the arts on both datasets.

Image and Video Captioning with Augmented Neural Architectures

Abstract: Neural-network-based image and video captioning can be substantially improved by utilizing architectures that make use of special features from the scene context, objects, and locations. A novel discriminatively trained evaluator network for choosing the best caption among those generated by an ensemble of caption generator networks further improves accuracy.

An exploration of the potential of Automatic Speech Recognition to assist and enable receptive communication in higher education

Abstract: The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking.