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New GIVE-KWS system uses visual speech to improve noise-robust keyword spotting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GIVE-KWS, a novel system for keyword spotting that leverages visual speech cues to improve robustness against noise. By incorporating a gated cross-attention mechanism, GIVE-KWS conditions query audio on lip motion, demonstrating significant performance gains. The system achieves a 72.9% reduction in unseen-keyword error rate at -10 dB SNR compared to the benchmark system, highlighting the importance of phonemic information in visual representations for effective noise-robustness. AI

IMPACT Enhances robustness of speech recognition systems in noisy environments, potentially improving user experience in real-world applications.

RANK_REASON The cluster contains a research paper detailing a new system and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GIVE-KWS system uses visual speech to improve noise-robust keyword spotting

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The cluster contains a research paper detailing a new system and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ming-Hsiang Hu, Kuan-Tang Huang, Hung-Shin Lee, Berlin Chen ·

    GIVE-KWS: Gated Injection of Visual Evidence for Noise-Robust Query-by-Example Keyword Spotting

    arXiv:2610.07046v1 Announce Type: cross Abstract: Visual speech promises noise-robust keyword spotting, yet a visual stream is not necessarily used. On a tri-modal query-by-example keyword spotting (QbyE-KWS) benchmark, we find that a system with a task-trained visual encoder com…

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