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Glossary

Terms used in accessibility research and practice. Each entry has a definition, common aliases, and category tags.

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Talking Head(also: Virtual Talking Head, Animated Face, 3D Talking Head)
A talking head is a computer-generated 3D or 2D animated representation of a human face and articulatory system that produces visible speech movements synchronised with audio output. In accessibility and speech therapy contexts, talking heads are particularly valuable because…
Teachable Object Recognition(also: Teachable Object Recognizer, TOR, Personalized Object Recognition)
A machine learning approach that allows users to train an object recognition system to identify their own personal items by providing a small number of training examples, typically photos or videos. This technology is particularly valuable for blind and low vision users who need…
Teachable Object Recognizer(also: Teachable Machine, Personalized Object Recognizer)
A machine learning application that allows end users to train custom object recognition models by providing their own example images, rather than relying on pre-trained models with fixed categories. In accessibility contexts, teachable object recognizers empower blind and…
Text Spotting(also: Scene Text Detection)
A computer vision technique that detects and localizes text within images in real time, without actually performing OCR recognition. Text spotting algorithms identify where text appears in a camera frame, its boundaries, and orientation. In accessibility applications, text…
Time-Causal Model(also: Temporal Causal Model, Sequential Logic Model)
A computational model that enforces temporal coherence in predictions by ensuring that the sequence of recognized events follows a logical causal order. In recipe tracking, a time-causal model prevents the system from predicting that an earlier step is currently happening after…
Transfer Learning
A machine learning technique where a model trained on a large general dataset is adapted to perform a new, more specific task using a much smaller amount of new training data. Rather than training a model from scratch, transfer learning leverages patterns already learned by an…

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