Listenattends

Listenattends is a term used in artificial intelligence and cognitive science to describe neural architectures that combine listening (processing of auditory input) with attention mechanisms that selectively focus on parts of the input. The term is not widely standardized and may appear in descriptions or discussions rather than as a formal nomenclature.

In practice, listenattends describes models that integrate an auditory encoder with an attention-based decoder. The listen component converts raw audio into a sequence of high-level representations; the attend component computes attention weights over these representations to highlight informative time frames or spectral features, enabling more accurate transcription, classification, or control signals.

Common applications include speech recognition, audio event detection, and multimodal systems where audio is combined with other inputs such as video or text. A well-known related concept is the Listen, Attend and Spell architecture for end-to-end speech recognition, which popularized the idea of end-to-end attention-based listening systems.

Variations differ in the choice of encoder type, attention mechanism (global vs. local), and the target outputs, ranging from character or word sequences to symbolic commands. The term listenattends sometimes appears in research discussions to emphasize the integration of listening and attending components, rather than as a distinct, standardized model family.

See also: attention mechanism, speech recognition, encoder-decoder models, LAS.