Listenattend

Listenattend is a term used to describe architectures and methodologies that couple listening, or acoustic signal processing, with attention mechanisms to selectively process portions of an input signal. In practice, the term is often used to refer to end-to-end speech recognition systems that align input audio frames with generated text through an attention module. The best-known instantiation is inspired by Listen, Attend and Spell (LAS), an encoder–decoder model that uses an attention mechanism to focus on different parts of the acoustic input while decoding characters or words.

Typically, a raw audio signal is converted into features such as log-Mel spectrograms. An encoder, which may be a recurrent neural network, convolutional neural network, or a Transformer, encodes the sequence into a high-level representation. A separate attention mechanism computes a context vector at each decoding step by weighting encoder outputs according to their relevance to the current output symbol. A decoder then generates text, occasionally using an auxiliary connectionist temporal classification (CTC) loss during training. Variants may employ location-based attention, multi-head attention, or streaming constraints to reduce latency.

Applications of listenattend models include automatic speech recognition, spoken language translation when combined with text generation, and audio captioning or event detection. In practice, these models aim to improve alignment between audio frames and output tokens, enabling better handling of long inputs, variability in speaking rate, and competing background noise.

Challenges include computational overhead, the need for large labeled datasets, and potential latency in real-time systems. The term listenattend is not a formal standard; it is used as a descriptive umbrella for models that integrate listening with attention, and its exact meaning may vary by author or application. See also LAS, attention mechanisms, speech recognition.