Interacting with Global Content

Interacting with Global Content

This research activity allows people to interact with digital content in ways that are more intuitive and that mimic the richness of human perception in all interaction. This research goes beyond text and speech-based exchange of content, to full multimodal interfaces that interpret information from a multitude of audio and visual cues. By furthering both the understanding and automatic analysis of human interaction with digital content and other humans, we are transforming the retrieval, understanding, and delivery of multimodal content for users.

In driving a more complete understanding of multimodal interaction between humans and for humans with digital content, we build automatic systems that track human engagement and affective response, and judge how best to retrieve and render responsive content for the user.

Research team

Publications

Interacting with Global Content

Social media based 3D visual popularity

  • Posted: 1 Apr 2017
  • Author: Rozenn Dahyot, Abdullah Bulbul
  • Publication: Computers and Graphics
Interacting with Global Content

Predictors of depression severity in a treatment-seeking sample

  • Posted: 1 Mar 2016
  • Author: Gavin Doherty, Derek Richards, Thomas Richardson, Ladislav Timulak, Noemi Vigana, Jacqueline Mooney, Claire Hayes, John Sharry
  • Publication: International Journal of Clinical and Health Psychology
Interacting with Global Content

User Interaction for Image Recolouring using L2A

  • Posted: 11 Dec 2017
  • Author: Rozenn Dahyot, M. Grogan, A. Smolic
  • Publication: CVMP 2017 - 14th European Conference on Visual Media Production
Interacting with Global Content

Retrieving and classifying instances of source code plagiarism

  • Posted: 13 Sep 2017
  • Author: , Gareth Jones, Ganguly, D., Ramirez-de-la-Cruz, A., Ramirez-de-la-Rosa, G., Villatoro-Tello, E.
  • Publication: Information Retrieval Journal

Research Goals

New methods are being developed to process both speech-only and audio-visual data, and train statistical engines to infer attentional state. The ability to track user engagement and interest in conversational interaction is key to reproducing natural interactions in the future, be that with a robot, personal assistant or avatar. The research explores what makes an avatar, or computer generated speaker, engaging to a user. The research uniquely combines ADAPT expertise on expressive synthesis, the role of paralinguistic cues in speech, and avatar animation.

Also addressed are issues of multimodal content relevant to interaction from two perspectives: The first addresses challenges of locating and isolating objects of interest in a visual stream and exploiting visual cues in speech to augment speech recognition capabilities. Learning techniques from unstructured multimodal data streams are also examined. The second is addressed by establishing methods for the exploitation of dialogue in user interaction in information retrieval, and to the exploitation of context to enable proactive information retrieval.

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