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.

Improving the Deaf community's access to prostate and testicular cancer information: a survey study.

Abstract: Members of the Deaf community face communication barriers to accessing health information. To resolve these inequalities, educational programs must be designed in the appropriate format and language to meet their needs. Deaf men (102) were surveyed before, immediately following, and two months after viewing a 52-minute prostate and testicular cancer video in American Sign Language (ASL) with open text captioning and voice overlay. To provide the Deaf community with information equivalent to that available to the hearing community, the video addressed two cancer topics in depth. While the inclusion of two cancer topics lengthened the video, it was anticipated to reduce redundancy and encourage men of diverse ages to learn in a supportive, culturally aligned environment while also covering more topics within the partnership's limited budget. Survey data were analyzed to evaluate the video's impact on viewers' pre- and post-intervention understanding of prostate and testicular cancers, as well as respondents' satisfaction with the video, exposure to and use of early detection services, and sources of cancer information. From baseline to immediately post-intervention, participants' overall knowledge increased significantly, and this gain was maintained at the two-month follow-up. Men of diverse ages were successfully recruited, and this worked effectively as a support group. However, combining two complex cancer topics, in depth, in one video appeared to make it more difficult for participants to retain as many relevant details specific to each cancer. Participants related that there was so much information that they would need to watch the video more than once to understand each topic fully. When surveyed about their best sources of health information, participants ranked doctors first and showed a preference for active rather than passive methods of learning. After viewing this ASL video, participants showed significant increases in cancer understanding, and the effects remained significant at the two-month follow-up. However, to achieve maximum learning in a single training session, only one topic should be covered in future educational videos.

Speech recognition based captioning system

Abstract: A system and associated method of converting audio data from a television signal into textual data for display as a closed caption on an display device is provided. The audio data is decoded and audio speech signals are filtered from the audio data. The audio speech signals are parsed into phonemes in accordance by a speech recognition module. The parsed phonemes are grouped into words and sentences responsive to a database of words corresponding to the grouped phonemes. The words are converted into text data which is formatted for presentation on the display device as closed captioned textual data.

Adversarial Inference for Multi-Sentence Video Description

Abstract: While significant progress has been made in the image captioning task, video description is still in its infancy due to the complex nature of video data. Generating multi-sentence descriptions for long videos is even more challenging. Among the main issues are the fluency and coherence of the generated descriptions, and their relevance to the video. Recently, reinforcement and adversarial learning based methods have been explored to improve the image captioning models; however, both types of methods suffer from a number of issues, e.g. poor readability and high redundancy for RL and stability issues for GANs. In this work, we instead propose to apply adversarial techniques during inference, designing a discriminator which encourages better multi-sentence video description. In addition, we find that a multi-discriminator "hybrid" design, where each discriminator targets one aspect of a description, leads to the best results. Specifically, we decouple the discriminator to evaluate on three criteria: 1) visual relevance to the video, 2) language diversity and fluency, and 3) coherence across sentences. Our approach results in more accurate, diverse, and coherent multi-sentence video descriptions, as shown by automatic as well as human evaluation on the popular ActivityNet Captions dataset.

A Multi-task Learning Approach for Image Captioning

Abstract: In this paper, we propose a Multi-task Learning Approach for Image Captioning (MLAIC ), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains. Specifically, MLAIC consists of three key components: (i) A multi-object classification model that learns rich category-aware image representations using a CNN image encoder; (ii) A syntax generation model that learns better syntax-aware LSTM based decoder; (iii) An image captioning model that generates image descriptions in text, sharing its CNN encoder and LSTM decoder with the object classification task and the syntax generation task, respectively. In particular, the image captioning model can benefit from the additional object categorization and syntax knowledge. To verify the effectiveness of our approach, we conduct extensive experiments on MS-COCO dataset. The experimental results demonstrate that our model achieves impressive results compared to other strong competitors.

Dynamic Subtitles: The User Experience

Abstract: Subtitles (closed captions) on television are typically placed at the bottom-centre of the screen. However, placing subtitles in varying positions, according to the underlying video content (`dynamic subtitles'), has the potential to make the overall viewing experience less disjointed and more immersive. This paper describes the testing of such subtitles with hearing-impaired users, and a new analysis of previously collected eye-tracking data. The qualitative data demonstrates that dynamic subtitles can lead to an improved User Experience, although not for all types of subtitle user. The eye-tracking data was analysed to compare the gaze patterns of subtitle users with a baseline of those for people viewing without subtitles. It was found that gaze patterns of people watching dynamic subtitles were closer to the baseline than those of people watching with traditional subtitles. Finally, some of the factors that need to be considered when authoring dynamic subtitles are discussed.