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


Interacting with Global Content

Bitrate classification of twice-encoded audio using objective quality features

  • Posted: 1 Jan 2016
  • Author: Naomi Harte, C. Sloan, D. Kelly, A. C. Kokaram, A. Hines
Interacting with Global Content

  • Posted: 5 Jul 2018
  • Author: Carl Vogel, Justine Reverdy, Akira Hayakawa
  • Publication: LREC 2018 - 11th International Conference on Language Resources and Evaluation
Interacting with Global Content

Incremental Joint Modelling for Dialogue State Tracking

  • Posted: 15 Aug 2017
  • Author: Robert Ross, Trinh, Anh Duong, Kelleher, John D
  • Publication: SemDial 2017 - 21st Workshop on the Semantics and Pragmatics of Dialogue
Interacting with Global Content

A review of eye gaze in virtual agents, social robots and HCI: Behaviour generation, user interaction and perception

  • Posted: 1 Jul 2015
  • Author: Rachel McDonnell, Kerstin Ruhland, Christopher E. Peters, Sean Andrist, Jeremy B. Badler, Norman L. Badler, Michael Gleicher, Bilge Mutlu
  • Publication: Computer Graphics Forum

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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