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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.11913/</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/1811.11913/feed.xml" rel="self" type="application/rss+xml" /><item><title>LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis — mentioned by PieSquared</title><link>https://news.ycombinator.com/item?id=20823438</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.11913:20823438</guid><pubDate>Wed, 28 Aug 2019 19:52:57 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s a few recent papers actually that show minor improvements by integrating LPC prediction into deep methods ([0], [1]). In my experience (some of which comes from reproducing these, some of which comes from my own experiments), this isn&amp;#x27;t actually too useful, at at most offers a minor modeling benefit. The main difference between something like Festival and what we have now is the amount of domain-specific engineering. (This is generally the promise of deep learning -- replace hand-engineered features with simple-to-understand features and a deep model.) If you go and read the Festival…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.11913/"&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>
