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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2501.11378/</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/2501.11378/feed.xml" rel="self" type="application/rss+xml" /><item><title>Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio — mentioned by rhdunn</title><link>https://news.ycombinator.com/item?id=49371085</link><guid isPermaLink="false">urn:hackerpapers:mention:2501.11378:49371085</guid><pubDate>Thu, 20 Aug 2026 06:27:00 GMT</pubDate><description>&lt;p&gt;Speech recognition is a notoriously difficult problem to solve. It can work great as a first pass that someone can fix up, but not on its own. This is why having sentence/clause and word-level transcription markers along side a recording help. One of the main issues is around homophones in an accent (Adam/Atom in American English, Bath/Barf in London English, etc.). Not to mention pronunciation variations due to fast speech, speech impedements, or parts of words side-by-side that sound like a different word. Another big issue is around misaligned training data. For example, Whisper is known…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2501.11378/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item></channel></rss>
