https://www.hlt.inesc-id.pt/wiki/index.php?action=history&feed=atom&title=Detecting_mis-recognitions_in_ASR_output 2026-05-31T06:05:07Z Revision history for this page on the wiki MediaWiki 1.41.0 https://www.hlt.inesc-id.pt/wiki/index.php?title=Detecting_mis-recognitions_in_ASR_output&diff=6089&oldid=prev 2010-12-23T16:07:09Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 16:07, 23 December 2010</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l24">Line 24:</td> <td colspan="2" class="diff-lineno">Line 24:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[category:Seminars]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[category:Seminars]]</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; 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;"><div>[[category:Seminars <del style="font-weight: bold; text-decoration: none;">2009</del>]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; 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;"><div>[[category:Seminars <ins style="font-weight: bold; text-decoration: none;">2010</ins>]]</div></td></tr> </table>

Acbm https://www.hlt.inesc-id.pt/wiki/index.php?title=Detecting_mis-recognitions_in_ASR_output&diff=5864&oldid=prev 2010-10-04T16:16:58Z

<p></p> <p><b>New page</b></p><div>__NOTOC__<br /> {{infobox|name= Thomas Pellegrini<br /> |username=thomas<br /> |contact=thomas<br /> |phone=+351-213-100-232<br /> |fax=+351-213-145-843<br /> }}<br /> <br /> == Date ==<br /> <br /> * 15:00, Friday, October 8&lt;sup&gt;th&lt;/sup&gt;, 2010<br /> * Room 336<br /> <br /> == Speaker ==<br /> <br /> * [[Thomas Pellegrini]]<br /> <br /> == Abstract ==<br /> <br /> Detecting incorrect words in automatic transcriptions can be useful for many applications: to mark or discard low-confidence words in automatic news subtitles or transcriptions, to select unsupervised material to train acoustic models, etc. In this talk, I will report experiments where various statistical classifiers were compared: a baseline Maximum<br /> Entropy approach, Conditional Random Fields, and a Markov Chain approach. New features gathered from other knowledge sources than the decoder itself were explored: a binary feature that compares outputs from two different ASR systems (word by word), a feature based on the number of hits of the hypothesized bigrams, obtained by queries entered into a very popular Web search engine, and finally a feature related to automatically infered topics at sentence and word levels. A classification error rate improvement from 13.9% to 12.1% was achieved. Experiments were conducted on a European Portuguese and an American English broadcast news corpus.<br /> <br /> '''Note:''' This seminar will be held in English, if required.<br /> <br /> [[category:Seminars]]<br /> [[category:Seminars 2009]]</div>

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