Department Publications

Precise temporal localization of named entities in speech is crucial for privacy-preserving audio processing. However, prevailing cascaded pipelines propagate transcription errors and end‐to‐end models lack the temporal granularity required for reliable frame‐level detection. To address these limitations, we introduce DEnSNEL (Dense End‐to‐end Spoken Named Entity Localizer), the first end-to-end model to perform direct, frame-level spoken named entity localization, without intermediate character or text representations. We reformulate the task as a dense, frame-wise binary classification ("entity" vs. "non-entity"), employing a lightweight encoder-classifier architecture. To improve boundary delineation, DEnSNEL incorporates a learnable complex filter bank to capture phonetic information, and employs a boundary-focused loss that explicitly optimizes span precision. On the SLUE Phase 2 benchmark …