Massive Open-Vocabulary Keyword Spotting
Automatic speech recognition systems have been shown to under-perform when it comes to transcribing words rarely seen in the training data, namely specialized terminology. Open-vocabulary keyword spotting, combined with contextual biasing, has been shown to mitigate this issue. However, existing systems can only handle glossaries of a few hundred terms without becoming an infeasible bottleneck. We propose a system that stores features with a memory footprint up to 128 times smaller than a comparable baseline and allows users to process massive databases while remaining open-vocabulary. Without fine-tuning the speech recognition model, our system achieves a comparable entity recall as unco...
| Authors | Leonor Barreiros, Raul Monteiro, Afonso Mendes et al. |
|---|---|
| Primary category | eess.AS |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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