Massive Open-Vocabulary Keyword Spotting
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Electrical Engineering and Systems Science > Audio and Speech Processing
Title:Massive Open-Vocabulary Keyword Spotting
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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