<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Speaker Recognition Based on Deep Learning: An Overview · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2012.00931/</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/2012.00931/feed.xml" rel="self" type="application/rss+xml" /><item><title>Speaker Recognition Based on Deep Learning: An Overview — mentioned by sva_</title><link>https://news.ycombinator.com/item?id=33892105</link><guid isPermaLink="false">urn:hackerpapers:mention:2012.00931:33892105</guid><pubDate>Wed, 07 Dec 2022 08:44:59 GMT</pubDate><description>&lt;p&gt;This is called speaker diarization, basically one of the 3 components of speaker recognition (verification, identification, diarization). You can do this pretty conveniently using pyannote-audio[0]. Coincidentally I did a small presentation on this at a university seminar yesterday :). I could post a Jupyter notebook if you&amp;#x27;re interested. PS: Bai &amp;amp; Zhang (2020) is a great review on the literature [1] [0] https://github.com/pyannote/pyannote-audio [1] https://arxiv.org/abs/2012.00931&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2012.00931/"&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>
