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Disney Research

Machine Learning & Data Analytics

Keyword Spotting in Multi-player Voice Driven Games for Children

This paper highlights the issues with keyword spotting using a simple two-word game played by children of different age groups and gives quantitative performance assessments using a novel keyword spotting technique that is especially suited to such scenarios.

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Evidence of Phonological Processes in Automatic Recognition of Children’s Speech

We describe phone recognition experiments on hand labelled data for children aged between 5 and 9.

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G-g-go! Juuump! Online Performance of a Multi-keyword Spotter in a Real-time Game

We report results for an online multi-keyword spotter in a game that contains overlapping speech, off-task side talk, and keyword forms that vary in completeness and duration.

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Multiplicative Representations for Unsupervised Semantic Role Induction

We propose a neural model to learn argument embeddings from the context by explicitly incorporating dependency relations as multiplicative factors, which bias argument embeddings according to their dependency roles.

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The Robot Who Knew Too Much: Toward Understanding the Privacy/Personalization Trade-off in Child-Robot Conversation

We explore what happens in the increasingly likely situation that a robot has sensed information about a child of which the child is unaware, then discloses that information in conversation in an effort to personalize the child’s experience.

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Assumed Density Filtering Methods for Scalable Learning of Bayesian Neural Networks

In this paper, we first rigorously compare the two algorithms and in the process develop several extensions, including a version of EBP for continuous regression problems and a PBP variant for binary classification.

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Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval

We showcase the efficacy of our approach in a user study, where we demonstrate orders-of-magnitude improvements in search quality compared to baseline systems.

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Person Re-identification using Deformable Patch Metric Learning

In this paper, we propose to learn appearance measures for patches that are combined using a spring model for addressing the correspondence problem.

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Exploiting View-Specific Appearance Similarities Across Classes for Zero-Shot Pose Prediction: A Metric Learning Approach

We propose a metric learning approach for joint class prediction and pose estimation.

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Parallel Detection of Conversational Groups of Free-Standing People and Tracking of their Lower-Body Orientation

We propose an alternating optimization procedure that estimates lower body orientations and detects groups of interacting people.

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