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		<id>http://index.cslt.org/mediawiki/index.php?action=history&amp;feed=atom&amp;title=Lantian_Li_14-10-27</id>
		<title>Lantian Li 14-10-27 - 版本历史</title>
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		<updated>2026-04-08T18:01:02Z</updated>
		<subtitle>本wiki的该页面的版本历史</subtitle>
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	<entry>
		<id>http://index.cslt.org/mediawiki/index.php?title=Lantian_Li_14-10-27&amp;diff=12100&amp;oldid=prev</id>
		<title>2014年10月27日 (一) 12:27 Lilt</title>
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				<updated>2014-10-27T12:27:45Z</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10月27日 (一) 12:27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. SVM training data: each true speaker test with the top N imposter making up the training data. N = 2,5,10.&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. SVM training data: each true speaker test with the top N imposter making up the training data. N = 2,5,10.&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;2. &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;Four &lt;/del&gt;types of scoring domain feature: 1).system score; 2). tnorm score; 3).system score + cohort scores; 4).system score + cohort score + detal scores;&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;2. &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;Five &lt;/ins&gt;types of scoring domain feature: 1).system score; 2). tnorm score; 3).system score + cohort scores; 4).system score + cohort score + detal scores;&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;5).system score + cohort score + detal scores + tnorm score;&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;5).system score + cohort score + detal scores + tnorm score;&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=Lantian_Li_14-10-27&amp;diff=12099&amp;oldid=prev</id>
		<title>Lilt：以“Weekly Summary  1. SVM training data: each true speaker test with the top N imposter making up the training data. N = 2,5,10.  2. Four types of scoring domain featur...”为内容创建页面</title>
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				<updated>2014-10-27T12:27:27Z</updated>
		
		<summary type="html">&lt;p&gt;以“Weekly Summary  1. SVM training data: each true speaker test with the top N imposter making up the training data. N = 2,5,10.  2. Four types of scoring domain featur...”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. SVM training data: each true speaker test with the top N imposter making up the training data. N = 2,5,10.&lt;br /&gt;
&lt;br /&gt;
2. Four types of scoring domain feature: 1).system score; 2). tnorm score; 3).system score + cohort scores; 4).system score + cohort score + detal scores;&lt;br /&gt;
&lt;br /&gt;
5).system score + cohort score + detal scores + tnorm score;&lt;br /&gt;
&lt;br /&gt;
3. For a given test set, the results show that for linear SVM, the EER of 3)/4) is similar and a little better than 2). and 5) gets the best performance. &lt;br /&gt;
&lt;br /&gt;
However, due to the test set is relatively small, the subsequent validation should be required. &lt;br /&gt;
&lt;br /&gt;
Another phenomenon is that there exists overfitting problem using 'rbf' and 'poly' kernel. The training effect is very good while the test result is so bad.&lt;br /&gt;
&lt;br /&gt;
Next Week&lt;br /&gt;
&lt;br /&gt;
1. Additional experiments shoule be done to prove the effectiveness of the method.&lt;br /&gt;
&lt;br /&gt;
2. Try to analyse the overfitting problem and solve it.&lt;/div&gt;</summary>
		<author><name>Lilt</name></author>	</entry>

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