Glossary

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Target Sound Extraction(also: 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 reference recording, or an embedding. Distinct from blind source separation (which…

Teachable AI(also: Teachable Machine Learning, Interactive Machine Learning)

Teachable AI refers to artificial intelligence systems that allow end users to personalize the system by providing their own training examples, high-level constraints, or prompts — without requiring programming or machine learning expertise. In the accessibility context,…

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-to-Audio(also: 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 'church bells ringing'. Distinct from text-to-speech, which produces spoken…

Topic Modeling(also: LDA, Latent Dirichlet Allocation)

A machine learning technique that automatically discovers abstract themes or topics within a collection of documents by analyzing patterns of word co-occurrence. Latent Dirichlet Allocation (LDA) is the most widely used topic modeling algorithm. In accessibility research, topic…

Training Data(also: Training Set, Training Dataset)

The collection of labeled examples used to teach a machine learning model to perform a specific task. The quality, quantity, and diversity of training data directly determine how well a model will perform. In accessibility contexts, training data quality is especially important…

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…

Transformer(also: 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 process entire input sequences in parallel using "self-attention" to weigh the…

Trigram(also: 3-gram)

A sequence of three consecutive words used in statistical language modeling for word prediction. Trigram models predict the next word based on the two preceding words, capturing more context than simpler unigram (single word) or bigram (two word) models. In AAC word prediction,…

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