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		<id>http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=140915_-_Lantian_Li</id>
		<title>140915 - Lantian Li - 版本历史</title>
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		<updated>2026-04-08T10:28:20Z</updated>
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	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=140915_-_Lantian_Li&amp;diff=11367&amp;oldid=prev</id>
		<title>2014年9月16日 (二) 02:14 Lilt</title>
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				<updated>2014-09-16T02:14:24Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class='diff diff-contentalign-left'&gt;
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				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;←上一版本&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;2014年9月16日 (二) 02:14的版本&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第3行：&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;第3行：&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;1. To help Thomas write the paper &amp;quot;an overview of robust speaker recognition&amp;quot; and finish the first part&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;1. To help Thomas write the paper &amp;quot;an overview of robust speaker recognition&amp;quot; and finish the first part&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;−&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;del class=&quot;diffchange diffchange-inline&quot;&gt; &lt;/del&gt;&amp;quot;Applcations&amp;quot;and the second part &amp;quot;speaker-dependent variablilty&amp;quot;, and then integrate the fisrt version.&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&amp;quot;Applcations&amp;quot;and the second part &amp;quot;speaker-dependent variablilty&amp;quot;, and then integrate the fisrt version.&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;2. To go on studying a scoring method on GMM-UBM aiming to design a cohort reference speaker models.&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;2. To go on studying a scoring method on GMM-UBM aiming to design a cohort reference speaker models.&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>Lilt</name></author>	</entry>

	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=140915_-_Lantian_Li&amp;diff=11366&amp;oldid=prev</id>
		<title>Lilt：以“Weekly Summary  1. To help Thomas write the paper &quot;an overview of robust speaker recognition&quot; and finish the first part   &quot;Applcations&quot;and the second part &quot;speaker-d...”为内容创建页面</title>
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				<updated>2014-09-16T02:14:08Z</updated>
		
		<summary type="html">&lt;p&gt;以“Weekly Summary  1. To help Thomas write the paper &amp;quot;an overview of robust speaker recognition&amp;quot; and finish the first part   &amp;quot;Applcations&amp;quot;and the second part &amp;quot;speaker-d...”为内容创建页面&lt;/p&gt;
&lt;p&gt;&lt;b&gt;新页面&lt;/b&gt;&lt;/p&gt;&lt;div&gt;Weekly Summary&lt;br /&gt;
&lt;br /&gt;
1. To help Thomas write the paper &amp;quot;an overview of robust speaker recognition&amp;quot; and finish the first part&lt;br /&gt;
&lt;br /&gt;
 &amp;quot;Applcations&amp;quot;and the second part &amp;quot;speaker-dependent variablilty&amp;quot;, and then integrate the fisrt version.&lt;br /&gt;
&lt;br /&gt;
2. To go on studying a scoring method on GMM-UBM aiming to design a cohort reference speaker models.&lt;br /&gt;
&lt;br /&gt;
Rearrange a test set and  clustering training set.  Complete data preparation task .&lt;br /&gt;
&lt;br /&gt;
Design the K-means algorithem to cluster the training set in order for organizing the cohort set, but still have bugs.&lt;br /&gt;
&lt;br /&gt;
Next Week&lt;br /&gt;
&lt;br /&gt;
1.Go on the task2 including K-means designing and scoring distribution statistics.&lt;/div&gt;</summary>
		<author><name>Lilt</name></author>	</entry>

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