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

Populating virtual cities using social media: Computer Animation and Virtual worlds

Interacting with Global Content

Radon transform of auditory neurograms: a robust feature set for phoneme classification

Conference

Multi-objective adversarial gesture generation

Conference

Patch-Based Colour Transfer with Optimal Transport

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.

Twitter
3 pm
@Adaptcentre
ADAPT Researcher at @AthloneIT @adriellenazar talked to @siliconrepublic's Science Uncovered about her work using… twitter.com/i/web/status/1…

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