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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2001.07263/</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/2001.07263/feed.xml" rel="self" type="application/rss+xml" /><item><title>Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard — mentioned by albertzeyer</title><link>https://news.ycombinator.com/item?id=24857188</link><guid isPermaLink="false">urn:hackerpapers:mention:2001.07263:24857188</guid><pubDate>Thu, 22 Oct 2020 12:15:45 GMT</pubDate><description>&lt;p&gt;This (https://arxiv.org/abs/2010.03449) is by far not the first publication which claims super human speech recognition, or human parity. For example https://arxiv.org/abs/1610.05256 or https://arxiv.org/abs/1703.02136. And since then, the WER has dropped much more on the same dataset (Switchboard), for example https://arxiv.org/abs/2001.07263. They highlight here the aspect of low latency, so this work might be current SOTA under this condition. Anyway, yes, we have reached human parity, or even surpassed it, for this dataset, under the same conditions. The conditions are: * English…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2001.07263/"&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>
