Search Results for author: Barry-John Theobald

Found 15 papers, 0 papers with code

Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning

no code implementations12 Nov 2022 Katherine Metcalf, Miguel Sarabia, Barry-John Theobald

In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks.

reinforcement-learning

Naturalistic Head Motion Generation from Speech

no code implementations26 Oct 2022 Trisha Mittal, Zakaria Aldeneh, Masha Fedzechkina, Anurag Ranjan, Barry-John Theobald

Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience.

On the role of Lip Articulation in Visual Speech Perception

no code implementations18 Mar 2022 Zakaria Aldeneh, Masha Fedzechkina, Skyler Seto, Katherine Metcalf, Miguel Sarabia, Nicholas Apostoloff, Barry-John Theobald

Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion of animation quality.

FedEmbed: Personalized Private Federated Learning

no code implementations18 Feb 2022 Andrew Silva, Katherine Metcalf, Nicholas Apostoloff, Barry-John Theobald

Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical.

Federated Learning

MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias

no code implementations9 Dec 2020 Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan, Ahmed Hussein Abdelaziz, Nicholas Apostoloff

Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.

Data Augmentation Face Generation +1

Modality Dropout for Improved Performance-driven Talking Faces

no code implementations27 May 2020 Ahmed Hussen Abdelaziz, Barry-John Theobald, Paul Dixon, Reinhard Knothe, Nicholas Apostoloff, Sachin Kajareker

We use subjective testing to demonstrate: 1) the improvement of audiovisual-driven animation over the equivalent video-only approach, and 2) the improvement in the animation of speech-related facial movements after introducing modality dropout.

Mirroring to Build Trust in Digital Assistants

no code implementations2 Apr 2019 Katherine Metcalf, Barry-John Theobald, Garrett Weinberg, Robert Lee, Ing-Marie Jonsson, Russ Webb, Nicholas Apostoloff

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user.

Learning Sharing Behaviors with Arbitrary Numbers of Agents

no code implementations10 Dec 2018 Katherine Metcalf, Barry-John Theobald, Nicholas Apostoloff

We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents.

Q-Learning

Which phoneme-to-viseme maps best improve visual-only computer lip-reading?

no code implementations3 Oct 2017 Helen L. Bear, Richard W. Harvey, Barry-John Theobald, Yuxuan Lan

A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes.

Lip Reading speech-recognition +1

Resolution limits on visual speech recognition

no code implementations3 Oct 2017 Helen L. Bear, Richard Harvey, Barry-John Theobald, Yuxuan Lan

Visual-only speech recognition is dependent upon a number of factors that can be difficult to control, such as: lighting; identity; motion; emotion and expression.

Lip Reading speech-recognition +1

Some observations on computer lip-reading: moving from the dream to the reality

no code implementations3 Oct 2017 Helen L. Bear, Gari Owen, Richard Harvey, Barry-John Theobald

In the quest for greater computer lip-reading performance there are a number of tacit assumptions which are either present in the datasets (high resolution for example) or in the methods (recognition of spoken visual units called visemes for example).

Lip Reading

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