Glossary
Searchable terminology from accessibility, web standards, and related fields.
80 results found in Machine Learning.
- Principal Component Analysis (PCA)
- A statistical technique that reduces the dimensionality of data by identifying the principal axes of variation in a dataset. In accessibility and assistive technology contexts, PCA is commonly used in…
- Prompt engineering (Prompt design, Prompt crafting)
- The practice of designing and iteratively refining natural language inputs to large language models to elicit more accurate, relevant, or useful responses. In accessibility contexts, prompt engineerin…
- Recurrent Neural Network (RNN)
- A recurrent neural network (RNN) is a type of artificial neural network designed to process sequential data by maintaining an internal state (memory) that captures information from previous inputs in …
- SHAP (SHapley Additive exPlanations)
- A unified framework for feature-importance explanations of machine-learning models, introduced by Lundberg and Lee in 2017, grounded in Shapley values from cooperative game theory. For any model and i…
- Semantic Segmentation (Pixel-Level Classification, Scene Parsing)
- A computer vision technique that classifies every pixel in an image into a predefined category, producing a detailed map of what objects are present and where they are located. Unlike object detection…
- Sequence-to-Sequence (Seq2Seq, Encoder-Decoder)
- A neural network architecture designed for tasks where both input and output are sequences of variable length, such as machine translation, speech recognition, and video captioning. A seq2seq model co…
- Sign Language Machine Translation (English-to-ASL Translation, Sign Language MT, Text-to-Sign Translation)
- The automatic translation of written or spoken text into a signed language (or vice versa) using computational methods, typically producing output as an animated signing avatar or, less commonly, as r…
- Speaker Adaptation (Voice Adaptation, Speaker-Adaptive Training, Voice Personalization)
- Speaker adaptation is the process of adjusting an existing automatic speech recognition (ASR) system — usually one trained on a large, demographically broad corpus of able-bodied speakers — to a parti…
- Speech Language Model (SLM, Audio Language Model, Speech Foundation Model)
- A class of large neural models that processes both speech and text in a single end-to-end framework, integrating tasks — automatic speech recognition, spoken language understanding, dialogue, speech g…
- Stable Diffusion
- An open-weights latent text-to-image diffusion model released by Stability AI in 2022. It operates by iteratively denoising a random latent tensor, conditioned on text embeddings produced by a frozen …
- Supervector (GMM Supervector)
- A supervector is a high-dimensional feature representation created by concatenating the mean vectors from all components of a Gaussian Mixture Model (GMM) adapted to a specific speaker or utterance. T…
- Target Sound Extraction (Target Sound Separation, TSE)
- A machine-learning task in which a model isolates a specific target sound (or class of sounds) from a complex acoustic mixture, conditioned on some specification of the target - a text label, a refere…
- Text-to-Audio (Text-to-Audio Generation, TTA)
- A class of generative AI models that synthesise non-speech sound (environmental sounds, sound effects, music stems) from a text prompt - for example producing the sound of 'leaves rustling in wind' or…
- Transformer (Transformer Model, Transformer Architecture)
- A deep learning architecture introduced by Vaswani et al. in 2017 that relies entirely on attention mechanisms rather than recurrence (RNNs) or convolution for sequence modeling tasks. Transformers pr…
- Universal Background Model (UBM)
- A Universal Background Model (UBM) is a large Gaussian Mixture Model trained on speech from many speakers to represent speaker-independent acoustic characteristics. The UBM serves as a reference distr…
- Vision Language Model (VLM, Vision-Language Model, Multimodal Large Language Model)
- A machine-learning model trained to take both images and natural-language text as input and to produce natural-language output. Modern VLMs — such as GPT-4o, Gemini, and Claude — can describe a photo,…
- Visual assistance technology (VAT, AI visual assistance, Visual interpretation service)
- Technology that uses artificial intelligence, computer vision, or human volunteers to provide visual information to blind and low-vision users. Examples include apps like Seeing AI, Be My Eyes, and Lo…
- Viterbi Algorithm
- The Viterbi algorithm is a dynamic-programming procedure for finding the most likely sequence of hidden states in a Hidden Markov Model given a sequence of observations. It is the standard solution to…
- Wav2Vec (Wav2Vec2, Wav2Vec 2.0)
- A family of self-supervised speech representation models from Meta AI that learn rich acoustic embeddings directly from raw waveform audio without requiring transcribed training data. Wav2Vec 2.0, int…
- YOLO (You Only Look Once) (YOLO, YOLOv8, YOLO Object Detector)
- A family of real-time object detection neural networks that predict bounding boxes and class labels in a single forward pass over an image, rather than using a two-stage propose-then-classify pipeline…