23 citations · 44 across the 10 of their papers we have counts for
13 papers
Latent Phrase Matching for Dysarthric Speech
Colin Lea, Dianna Yee, Jaya Narain +4
Many consumer speech recognition systems are not tuned for people with speech disabilities, resulting in poor recognition and user experience, especially for severe speech differen…
From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition
Colin Lea, Zifang Huang, Lauren Tooley +6
Consumer speech recognition systems do not work as well for many people with speech diferences, such as stuttering, relative to the rest of the general population. However, what is…
Reflow: Automatically Improving Touch Interactions in Mobile Applications through Pixel-based Refinements
Jason Wu, Titus Barik, Xiaoyi Zhang +3
Touch is the primary way that users interact with smartphones. However, building mobile user interfaces where touch interactions work well for all users is a difficult problem, bec…
Nonverbal Sound Detection for Disordered Speech
Colin Lea, Zifang Huang, Dhruv Jain +5
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-graine…
Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
Vikramjit Mitra, Zifang Huang, Colin Lea +8
Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice op…
SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter
Colin Lea, Vikramjit Mitra, Aparna Joshi +2
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 fo…
Audio- and Gaze-driven Facial Animation of Codec Avatars
Alexander Richard, Colin Lea, Shugao Ma +3
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 al…
Temporal Convolutional Networks for Action Segmentation and Detection
Colin Lea, Michael D. Flynn, Rene Vidal +2
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decoup…
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Colin Lea, Rene Vidal, Austin Reiter +1
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 Ne…
SANTIAGO: Spine Association for Neuron Topology Improvement and Graph Optimization
William Gray Roncal, Colin Lea, Akira Baruah +1
Developing automated and semi-automated solutions for reconstructing wiring diagrams of the brain from electron micrographs is important for advancing the field of connectomics. Wh…
Recognizing Surgical Activities with Recurrent Neural Networks
Robert DiPietro, Colin Lea, Anand Malpani +5
We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities,…
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Colin Lea, Austin Reiter, Rene Vidal +1
Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, des…
Efficient Hierarchical Markov Random Fields for Object Detection on a Mobile Robot
Colin S. Lea, Jason J. Corso
Object detection and classification using video is necessary for intelligent planning and navigation on a mobile robot. However, current methods can be too slow or not sufficient f…