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Found 10 papers, 3 papers with code
Latent Phrase Matching for Dysarthric Speech
Many consumer speech recognition systems are not tuned for people with speech disabilities, resulting in poor recognition and user experience, especially for severe speech differences.
Nonverbal Sound Detection for Disordered Speech
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.
Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
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.
SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter
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.
Audio- and Gaze-driven Facial Animation of Codec Avatars
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 for Action Segmentation and Detection
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
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).
Ranked #6 on
Action Segmentation
on JIGSAWS
SANTIAGO: Spine Association for Neuron Topology Improvement and Graph Optimization
Our automated approach improves the local subgraph score by more than four times and the full graph score by 60 percent.
Recognizing Surgical Activities with Recurrent Neural Networks
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.
Ranked #1 on
Surgical Skills Evaluation
on MISTIC-SIL
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier.
Ranked #7 on
Action Segmentation
on JIGSAWS
