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Massive Open-Vocabulary Keyword Spotting

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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2606.11279 (eess)
[Submitted on 9 Jun 2026]

Title:Massive Open-Vocabulary Keyword Spotting

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Abstract: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 uncompressed solutions, even in languages not seen during training.
Comments: Accepted to Interspeech 2026
Subjects: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2606.11279 [eess.AS]
  (or arXiv:2606.11279v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2606.11279
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Leonor Machado Barreiros [view email]
[v1] Tue, 9 Jun 2026 12:11:11 UTC (183 KB)
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