We propose a new method for improving the presentation of subtitles in video (e.g., TV and movies). With conventional subtitles, the viewer has to constantly look away from the main viewing area to read the subtitles at the bottom of the screen, which disrupts the viewing experience and causes unnecessary eyestrain. Our method places on-screen subtitles next to the respective speakers to allow the viewer to follow the visual content while simultaneously reading the subtitles. We use novel identification algorithms to detect the speakers based on audio and visual information. Then the placement of the subtitles is determined using global optimization. A comprehensive usability study indicated that our subtitle placement method outperformed both conventional fixed-position subtitling and another previous dynamic subtitling method in terms of enhancing the overall viewing experience and reducing eyestrain.
@cite_5: Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.
@cite_1: Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.
@cite_6: This paper reviews definitions of audio-visual synchrony and examines their empirical behaviour on test sets up to 200 times larger than used by other authors. The results give new insights into the practical utility of existing synchrony definitions and justify application of audio-visual synchrony techniques to the problem of active speaker localisation in broadcast video. Performance is evaluated using a test set of twelve clips of alternating speakers from the multiple speaker CUAVE corpus. Accuracy of 76 is obtained for the task of identifying the active member of a speaker pair at different points in time, comparable to performance given by two purely video image-based schemes. Accuracy of 65 is obtained on the more challenging task of locating a point within a 100×100 pixel square centered on the active speaker's mouth without no prior face detection; the performance upper bound if perfect face detection were available is 69 . This result is significantly better than two purely video image-based schemes.
Speaker detection in video is an active research topic. Speaker diarization is the problem of determining who spoke when based on audio signals @cite_1 . The problem of speech recognition based on visual information has been studied by gordan2002support and saenko2004articulatory,saenko2005visual . Visual and audio data are often used together in speaker localization ( @cite_6 @cite_5 ). Generally speaking, methods based on training data are more accurate but are more time-consuming. Out of efficiency consideration, we developed a method without involving a training process but still achieved robust detection results because of our use of several novel features and cues.