Researchers Develop Efficient Open-Vocabulary Keyword Spotting System for Speech Recognition
Researchers have created a keyword spotting system that can handle massive glossaries while maintaining a 128 times smaller memory footprint than comparable approaches. The system addresses a key limitation in automatic speech recognition: poor performance on specialized terminology and rare words. This advancement enables practical deployment of open-vocabulary speech systems for specialized domains without requiring model fine-tuning.
A new system for open-vocabulary keyword spotting has been developed to improve automatic speech recognition performance on specialized terminology and rarely-seen words. The approach combines keyword spotting with contextual biasing techniques, which have previously shown promise but were limited to glossaries of only a few hundred terms due to computational constraints. The proposed system achieves a 128-fold reduction in memory footprint compared to baseline solutions while maintaining comparable entity recall rates. Notably, the system performs well even in languages not encountered during training, and does not require fine-tuning of the underlying speech recognition model. The research has been accepted for presentation at Interspeech 2026, a major conference in speech processing.
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- arXiv cs.LGCenter
Massive Open-Vocabulary Keyword Spotting
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