Search Results for author: Colin Lea

Found 9 papers, 3 papers with code

Nonverbal Sound Detection for Disordered Speech

no code implementations15 Feb 2022 Colin Lea, Zifang Huang, Dhruv Jain, Lauren Tooley, Zeinab Liaghat, Shrinath Thelapurath, Leah Findlater, Jeffrey P. Bigham

Voice assistants have become an essential tool for people with various disabilities because they enable complex phone- or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens.

Event Detection Sound Event Detection

Analysis and Tuning of a Voice Assistant System for Dysfluent Speech

no code implementations18 Jun 2021 Vikramjit Mitra, Zifang Huang, Colin Lea, Lauren Tooley, Sarah Wu, Darren Botten, Ashwini Palekar, Shrinath Thelapurath, Panayiotis Georgiou, Sachin Kajarekar, Jefferey Bigham

Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice operated systems do not work.

speech-recognition Speech Recognition +1

SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter

no code implementations24 Feb 2021 Colin Lea, Vikramjit Mitra, Aparna Joshi, Sachin Kajarekar, Jeffrey P. Bigham

The ability to automatically detect stuttering events in speech could help speech pathologists track an individual's fluency over time or help improve speech recognition systems for people with atypical speech patterns.

Event Detection speech-recognition +1

Audio- and Gaze-driven Facial Animation of Codec Avatars

no code implementations11 Aug 2020 Alexander Richard, Colin Lea, Shugao Ma, Juergen Gall, Fernando de la Torre, Yaser Sheikh

Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i. e., for virtual reality), and are almost indistinguishable from video.

Temporal Convolutional Networks: A Unified Approach to Action Segmentation

1 code implementation29 Aug 2016 Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager

The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Neural Network that encode spatiotemporal information locally, and second, input these features into a classifier that captures high-level temporal relationships, such as a Recurrent Neural Network (RNN).

Action Segmentation

SANTIAGO: Spine Association for Neuron Topology Improvement and Graph Optimization

no code implementations8 Aug 2016 William Gray Roncal, Colin Lea, Akira Baruah, Gregory D. Hager

Our automated approach improves the local subgraph score by more than four times and the full graph score by 60 percent.

Computer Vision

Recognizing Surgical Activities with Recurrent Neural Networks

3 code implementations20 Jun 2016 Robert DiPietro, Colin Lea, Anand Malpani, Narges Ahmidi, S. Swaroop Vedula, Gyusung I. Lee, Mija R. Lee, Gregory D. Hager

In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks.

Gesture Recognition

Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation

no code implementations9 Feb 2016 Colin Lea, Austin Reiter, Rene Vidal, Gregory D. Hager

We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier.

Action Classification Action Segmentation +2

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