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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Open-vocabulary Keyword-spotting with Adaptive Instance Normalization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2309.08561/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2309.08561/feed.xml" rel="self" type="application/rss+xml" /><item><title>Open-vocabulary Keyword-spotting with Adaptive Instance Normalization — mentioned by GavCo</title><link>https://news.ycombinator.com/item?id=43544345</link><guid isPermaLink="false">urn:hackerpapers:mention:2309.08561:43544345</guid><pubDate>Tue, 01 Apr 2025 08:46:40 GMT</pubDate><description>&lt;p&gt;I was wondering the same and found these related papers: https://arxiv.org/pdf/2309.08561 https://arxiv.org/pdf/2406.02649 I haven&amp;#x27;t really dug in yet but from a quick skim, it looks promising. They show a big improvement over Whisper on a medical dataset (F1 increased from 80.5% to 96.58%). The inference time for the keyword detection is about 10ms. If it scales linearly with additional keywords you could potentially scale to hundreds or thousands of keywords but it really depends on how sensitive you are to latency. For real-time with large vocabularies my guess is you might still want to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2309.08561/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>eess.AS</category></item></channel></rss>
